Comprehensive Evaluation Value Research Articles

Optimization of Flavonoid Extraction from Abelmoschus manihot Flowers Using Ultrasonic Techniques: Predictive Modeling through Response Surface Methodology and Deep Neural Network and Biological Activity Assessment.

Understanding the optimal extraction methods for flavonoids from Abelmoschus manihot flowers (AMF) is crucial for unlocking their potential benefits. This study aimed to optimize the efficiency of flavonoid extraction from AMF. After comparing extraction methods, the ultrasonic cell crusher demonstrated superior performance over conventional techniques. Four key factors-solid-to-liquid ratio (1:10 to 1:50 g·mL-1), ethanol concentration (55% to 95%), ultrasonic time (10 to 50 min), and ultrasonic power (5% to 25% of 900 W)-were investigated and normalized using the entropy weight method. This led to a comprehensive evaluation (CE). Optimization of extraction conditions for the ultrasonic cell crusher was achieved through response surface methodology and a deep neural network model, resulting in optimal parameters: ethanol volume fraction of 66%, solid-to-liquid ratio of 1:21 g/mL, extraction efficiency of 9%, and extraction duration of 35 min, yielding a CE value of 23.14 (RSD < 1%). Additionally, the inhibitory effects of the optimized extracts against Streptococcus mutans (S. mutans) were assessed. The results revealed that AMF extract (AMFE) exhibits inhibitory effects on S. mutans, with concomitant inhibition of sucrase and lactate dehydrogenase (LDH). The MIC of AMFE against planktonic S. mutans is 3 mg/mL, with an MBC of 6 mg/mL. Within the concentration range of 1/8 MIC to 2 MIC of AMFE, the activities of sucrase and LDH decreased by 318.934 U/mg prot and 61.844 U/mg prot, respectively. The antioxidant activity of AMFE was assessed using the potassium ferricyanide reduction and phosphomolybdenum methods. Additionally, the effect of AMFE on DPPH, ABTS, and ·OH free radical scavenging abilities was determined. The concentrations at which AMFE exhibited over 90% scavenging rate for ABTS and DPPH free radicals were found to be 0.125 mg/mL and 2 mg/mL, respectively.

Optimizing the performance of TPCB/SCA composite-modified asphalt using improved response surface methodology

Abstract A composite-modified asphalt was developed by incorporating tire pyrolytic carbon black (TPCB) with a silane coupling agent (SCA) to enhance its ductility and storage stability. The functional binding between asphalt and TPCB was improved by the addition of SCA. The gray correlation comprehensive evaluation method is incorporated into the response surface methodology (RSM), leading to an improved RSM. The improved RSM was employed to optimize the content of the modifier and the preparation process to enhance overall performance. A central composite design-based experiment was conducted to optimize the penetration, softening point, 10°C ductility, Brookfield rotational viscosity, and storage stability indicators of modified asphalt. The response surface of the gray comprehensive evaluation value of the evaluation indicators was calculated using the gray correlation analysis method, and a second-order regression model was established between independent variables such as TPCB, SCA content, and shear time factor and the gray comprehensive evaluation value. This model analyzed the impact of a single variable and variable interaction on the performance of the TPCB/SCA composite-modified asphalt. The optimized model results showed that the preparation parameters that optimize the expected comprehensive performance of the composite-modified asphalt are 9.2% TPCB content, 2.1% SCA content, and a shear time of 56.3 min. Finally, the TPCB/SCA composite-modified asphalt prepared with the improved RSM exhibited the highest gray comprehensive evaluation value and the best overall performance, indicating the feasibility of the optimized preparation parameters with the improved RSM.

Deep reinforcement learning for multi-objective game strategy selection

Multi-objective game (MOG) is a fundamental model for the decision-making problems in which each player must consider multi-dimensional payoffs that reflect different objectives. Typically, solving MOG involves refining the set of equilibrium strategies, which is also known as MOG strategy selection (MOGS). However, existing MOG algorithms only allow one metric for MOGS, which limits the application in real-world scenarios where the players may have different preferences over multiple metrics. In this paper, we first develop a preference-based MOGS framework to encompass multiple metrics with different preferences in MOGS. Based on the framework, we introduce the concept of comprehensive evaluation value (CEV) to evaluate the quality of a strategy set given the preference of each metric. Using CEV as a reward signal, we formulate the problem of finding the optimal strategy set as a Markov decision process, and use deep reinforcement learning to train a policy for MOG strategy selection given the metrics and the corresponding preferences. Specifically, we combine a rational strategy filtering procedure with a Transformer-based encoder–decoder policy network to refine the strategies given the preferences, and then we use a revised REINFORCE algorithm to train the policy network. Besides, we introduce variable beam search decoding to improve the quality of a rollout by keeping track of the most promising strategy sets and choosing the best one. We benchmark our algorithm on the MOG instances generated by GAMUT, and extensive experiments demonstrate that our algorithm can generate the strategy set significantly better than the state-of-the-art baselines with lower computational overhead given different preferences. Furthermore, we compare our approach on real-world problems, showing the great advantages in both performance and runtime.

An Analysis of the Main Nutrient Components of the Fruits of Different Macadamia (Macadamia integrifolia) Cultivars in Rocky Desertification Areas and a Comprehensive Evaluation of the Mineral Element Contents

This study aims to understand the main nutrient composition and comprehensively evaluate the differences in the mineral element contents of fruits of different macadamia cultivars, as well as screen good cultivars that are suitable for use in rocky desert mountains. Nine macadamia nut cultivars were selected as test materials in rocky desert mountain orchards. The contents of crude fat, crude protein, and total soluble sugar in kernels and N, P, K, Ca, Mg, Fe, Mn, Cu, Zn, and B in peels and kernels were determined, respectively. Then, the kernels’ mineral element contents were comprehensively evaluated based on principal component analysis. The results showed that the kernels were rich in crude fat, protein, and soluble sugar, with the crude fat content reaching 75% or greater, and the variation among cultivars was small. However, the variation in soluble sugar content was extensive. The content of mineral elements varied in different cultivars and parts of the fruit, with the average macronutrient content being K &gt; N &gt; Ca &gt; P &gt; Mg in the pericarp and N &gt; K &gt; P &gt; Mg &gt; Ca in the kernel, and the content of micronutrients in the pericarp and the kernel being Mn &gt; Fe &gt; Zn &gt; Cu &gt; B. By principal component analysis, the 10 mineral nutrient indexes were calculated as four principal components, with a cumulative contribution rate of 88.051%. Using the affiliation function value method and the calculation of the comprehensive evaluation value, the nine cultivars could be classified into three categories. The cultivar with the highest comprehensive evaluation value of the mineral element content was O.C. The one with the lowest value was H2, which indicated that O.C is a suitable variety for popularization in rocky desert mountainous areas. Stepwise regression analysis concluded that P, K, Fe, Mn, and Cu were the indicators significantly influencing the mineral element content of macadamia nuts and fruits in rocky desert mountains.

Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction

To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into two types based on their content: one reflects the effectiveness of an alternative implementation and the other reflects the public expectations. Secondly, the two types of public opinions are sorted separately by linguistic variables. The evaluation of alternatives and the evaluation of expectations in different attributes are determined, both of which are expressed in the form of linguistic distributions. These two evaluations are then compared to determine the public satisfaction of the attributes in different alternatives. Thirdly, based on the deviation of public satisfaction in different attributes, a weight optimization model is constructed to determine the attribute weights. Fourthly, leveraging the interval credibility of attribute satisfaction for various alternatives, an evidential reasoning non-linear optimization model is established to obtain the comprehensive utility evaluation value for each alternative, which is used for ranking. Finally, a numerical example is employed to validate the feasibility and effectiveness of the proposed approach. According to the results of the numerical example, it can be concluded that the proposed approach can be effectively applied to large group decision-making problems that consider public satisfaction. Based on the comparison of methods, the proposed approach has certain advantages in reflecting public opinions and setting reference points, which can ensure the reliability of the decision results.

Effects of nitrogen fertilization rate and seeding density on the forage yield and quality of autumn‐sown triticale in an alpine grazing area of the Qinghai‐Tibetan Plateau, China

Abstract‘Gannong No. 2’ triticale (×Triticosecale Wittmack) is a cold‐resistant annual forage crop variety, which can be planted as artificial pasture to solve the problem of forage shortage in the Gannan alpine grazing area of the Qinghai‐Tibetan Plateau. However, the optimal nitrogen fertilization rate and seeding density for obtaining high yield and quality forage are still unknown. In this study, the effects of nitrogen fertilization rate and seeding density on the forage yield and quality of autumn‐sown ‘Gannong No. 2’ triticale were studied. We used a split‐plot design, with five nitrogen fertilization rates assigned to the main plots, namely 0 (A1), 120 (A2), 240 (A3), 360 (A4), and 480 kg N ha−1 (A5), and five seeding densities assigned to subplots: 506 × 104 (B1), 675 × 104 (B2), 843 × 104 (B3), 1011 × 104 (B4), and 1180 × 104 seeds ha−1 (B5). Significant differences in lodging rate, tiller number, hay yield, crude protein content, neutral detergent fibre, and relative feed value were observed at different fertilization levels. Significant differences were found in lodging rate, plant height, tiller number, hay yield, contents of neutral detergent fibre and acid detergent fibre, dry matter digestibility, and relative feed value among the seeding density treatments. The interaction of nitrogen fertilization rate and seeding density significantly affected forage yield and quality, with the A3B3 treatment having the highest comprehensive evaluation value. In alpine grazing areas of the Qinghai‐Tibetan Plateau, 240 kg N ha−1 and 843 × 104 seeds ha−1 should therefore be used for autumn‐sown triticale.

Dynamic Comprehensive Evaluation of a 660 MW Ultra-Supercritical Coal-Fired Unit Based on Improved Criteria Importance through Inter-Criteria Correlation and Entropy Weight Method

To address the issue of traditional static evaluation models being unable to comprehensively analyze the performance of ultra-supercritical coal-fired units under varying loads, we propose a dynamic comprehensive evaluation model based on the improved Criteria Importance Through Inter-criteria Correlation (CRITIC) method and entropy weight method (EWM). The comprehensive performance evaluation index system of ultra-supercritical coal fired units is constructed by examining the boiler performance, turbine performance, plant power performance, environmental performance, and flexible performance of coal-powered units. The CRITIC and EWM methods are used to calculate the weights of the indicators, which are then combined with the static evaluation results. Using a dynamic comprehensive evaluation model, we analyze ultra-supercritical coal-fired units, taking into account time weight. This allows us to obtain the comprehensive dynamic real-time evaluation value of the units under different loads. The research indicates that the weight of the evaluation index is changed when using the dynamic comprehensive evaluation model of the improved CRITIC and EWM. The index with lower weight is increased by 6.2%, while the index with higher weight is decreased by 0.22%. This alteration in weight range can provide a more objective reflection of the relationship between evaluation indicators. This model offers significant advantages in improving evaluation accuracy, weight balance distribution, and generality.

Research on the Impact of Artificial Intelligence on College Students' Learning Based on the Entropy Power Method

With the development of society，artificial intelligence [1] has gradually penetrated into all aspects of our lives, and has had different degrees of impact on different aspects of society. The development of artificial intelligence has a different degree of impact on all aspects of society and also affects the learning life of college students. In order to understand the impact of artificial intelligence on college students' learning at different levels, this paper designs a questionnaire to interview and analyse it, obtains the survey feedback results and analyzes and evaluates the results according to the survey. We first transformed the questionnaire data from textual data to numerical data，and invalid sample exclusion through case analysis. A total of 145 invalid samples were excluded to avoid errors in the post analysis. The data were validated for reliability analysis of this non-scale question based on the Cronbach_alpha coefficient. After that, determining the purpose of their own indicators - the impact of AI on students' learning, based on the purpose of the selected indicators, the questions in the questionnaire were screened, and questions that were considered to be the same indicator were grouped together to create "proficiency in learning software", "Dependence on AI learning tools", "Demand for the use of AI learning tools", "Tolerance for the use of AI learning tools", and "Acceptability of the future direction of educational development of AI" five indicators, and separately homogenise and assign points to the various indicators constituted by the selected questions, and finally form a complete evaluation system. We analysed the validity of all the questions in the questionnaire to derive the weights of the questions within the indicator. Calculate the evaluation value of each indicator, then differentiate the five indicators constructed in the second question into positive and negative indicators. An analysis of the weights of the five indicators based on the entropy weighting method reveals that the weight of the indicator "proficiency in learning software" is as high as 51 per cent. Finally, the five indicators are combined to calculate a composite evaluation value for the sample. Using Origin to make a comprehensive evaluation value line graph of the sample, analysing both the fluctuation of the value of the lines and the degree of intensity, and finally concluding that AI has a wide range of influence and a large degree of influence on college students' learning.

Coupled Coordination and Drivers of Green Technology Innovation and Carbon Emission Efficiency

The coupled and coordinated development of green technology innovation and carbon emission efficiency in the Yangtze River Economic Belt is crucial for its realization of green and low-carbon transformation. Based on the panel data of the Yangtze River Economic Belt from 2011 to 2021, the comprehensive evaluation levels of green technological innovation and carbon emission efficiency were measured, and the coupling degree of coordination model and panel Tobit model were constructed to empirically analyze the coupling degree of coordination and driving factors of the two. The results show that, overall, the comprehensive evaluation value of green technology innovation level in the Yangtze River Economic Zone shows an upward trend; the value of carbon emission efficiency first rises and then falls. From the perspective of spatial distribution, both of them present the characteristic of “high in the east and low in the west”. The coupling coordination degree shows a growing trend and initially realizes the basic coordination. The coupling degree of coordination shows a significant negative correlation between the energy structure and the coupling degree of both of them, while urbanization level, environmental regulation, industrial structure, level of economic development, degree of openness, and labor level all show a significant positive correlation.

Sustainability evaluation of urban large-scale infrastructure construction based on dynamic fuzzy cognitive map

Enhancing the sustainability of large-scale infrastructure construction is the foundation and trend of urban sustainable development and environmental protection. This paper proposes a perception and evaluation method based on Dynamic Fuzzy Cognitive Mapping (D-FCM) to address the dynamics, uncertainties, and implementation difficulties of sustainability evaluation in large-scale infrastructure construction. Using Hebbian rules to learn the experience and knowledge of experts, a dynamic evolution correlation matrix about evaluation indicators is obtained, and a D-FCM dynamic system model is established to obtain the sustainability evaluation results of large-scale infrastructure construction. The comprehensive sustainable evaluation value (K) of the selected subway project case is 0.934, which belongs to the V level in the sustainable level classification standard and has a good trend in sustainable development. Social public participation (S1), light pollution prevention (H1), wastewater discharge compliance rate (H2), soil pollution prevention (H3), and soil and water conservation and utilization (Z2) are the most significant and sensitive indicators for the sustainable development of large-scale infrastructure construction. Based on the interaction of influencing factors in the D-FCM model, an economically efficient control measures and decision-making plan can be proposed to improve the sustainability of large-scale infrastructure construction projects.

Differential Water Conservation Capacity in Broadleaved and Mixed Forest Restoration in Latosol Soil-Eroded Region, Hainan Province, China.

The water conservation capacity of the litter and soil layers of forest ecosystems improves the function of forest ecosystems in conserving soil and water. Plantation restoration plays a key role in preventing soil erosion. In order to evaluate the water conservation capacity of plantation restoration in Latosol soil-eroded region, we analyzed the litter thickness and mass, water absorption process, water holding recovery process, and soil water holding capacity of five restoration types (Hevea brasiliensis, Acacia mangium, Eucalyptus robusta, Acacia-Eucalyptus, and Acacia-Hevea) in the Mahuangling Watershed, Hainan province. The results showed that the thickness of the litter ranged from approximately 3.42 ± 0.24 to 4.73 ± 0.81 cm, and the litter mass ranged from 5.04 ± 1.52 t·ha-1 to 13.16 ± 1.76 t·ha-1, with higher litter mass in the SL layer than in the UL layer. The litter mass of A. mangium was higher than that of H. brasiliensis, E. robusta, Acacia-Eucalyptus, and Acacia-Hevea, which was 3.16 ± 1.76 t·ha-1. A. mangium forest was significantly higher than other plantation restoration types in terms of the maximum water retention capacity (Qmax) and the effective water retention capacity (Qeff). The soil bulk weight ranged from approximately 1.52 ± 0.09 to 1.59 ± 0.08 g·cm-3, and porosity ranged from 31.77 ± 4.72 to 35.62 ± 3.02%, both of which increased with the depth of the soil layer. The water holding capacity of 0-60 cm soil varied from approximately 12.94 ± 7.91 to 45.02 ± 31.79 t·ha-1, with A. mangium having the best soil permeability and the strongest soil water holding capacity. The entropy weight method was used to conduct a comprehensive evaluation, and the results showed that the water conservation capacity of the soil layer was 1.26 times higher than that of the litter layer, in which the water conservation capacity of A. mangium was the strongest, with a comprehensive evaluation value of 0.2854, which effectively intercepted rainfall and reduced surface runoff. Hence, we suggest that the planting of A. mangium should be considered in future ecological restoration projects of the erosion area of Mahuangling in order to improve the function of conserving soil and water in a restoration forest ecosystem.

A novel group decision-making method for interval-valued q-rung dual hesitant fuzzy information using extended power average operator and Frank operations

This paper advances the field of multi-attribute group decision making (MAGDM) by proposing a novel framework based on interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs). IVq-RDHFSs, which surpass most existing fuzzy sets, effectively represent complex fuzzy information by describing membership and non-membership degrees through interval value sets. However, prior MAGDM methods based on IVq-RDHFSs have been limited by the functions of operation rules and aggregation operators (AOs). This limitation is addressed through the construction of a new MAGDM framework, leveraging the robust Frank t-norm and t-conorm (FTT) operation and the extended power average (EPA) operator. The proposed framework features the interval-valued q-rung dual hesitant fuzzy Frank weighted extended power average (IVq-RDHFFWEPA) operator to obtain comprehensive evaluation values. The paper also introduces novel techniques for determining the weights of decision-makers and attributes. Practical applications of the proposed method are demonstrated through the assessment of desalination technology selection and rural green eco-tourism projects. Sensitivity and comparison analyses validate the superior functionality, accuracy, and flexibility of this method compared to many state-of-the-art methods. The contributions of this paper are two-fold: it develops efficient measurement techniques for IVq-RDHFSs, such as distance and weight calculation, and it introduces a comprehensive MAGDM method by integrating FTT and EPA under IVq-RDHFSs, which improves the efficiency of solving decision-making problems.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies, an emergency decision-making method based on multi-granularity language information is proposed. Firstly, decision makers select the appropriate language phrase set according to their own situation, give the preference information of the weight of each key indicator, and then transform the multi-granularity language information through consistency. On this basis, the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator. Subsequently, the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence, and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme. Lastly, the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

Multivariate Analysis Compares and Evaluates Heat Tolerance of Potato Germplasm.

High temperature is the most important environmental factor limiting potato (Solanum tuberosum L.) yield. The tuber yield has been used to evaluate the heat tolerance of some potato cultivars, but potato yield was closely correlated with the maturation period. Therefore, it is necessary to employ different parameters to comprehensively analyze and evaluate potato tolerance to heat stress. This study aimed to investigate physiologic changes during growth and development, and develop accurate heat tolerance evaluation methods of potato cultivars under heat stress. About 93 cultivars (including foreign elite lines, local landraces and cultivars) were screened using an in vitro tuber-inducing system (continuous darkness and 8% sucrose in the culture medium) under heat stress (30 °C) and normal (22 °C) conditions for 30 days. The tuber yield and number decreased significantly under heat stress compared to the control. A total of 42 cultivars were initially selected depending on tuber formation, after in vitro screening, further testing of selected cultivars was conducted in ex vitro conditions. The screened cultivars were further exposed to heat stress (35 °C/28 °C, day/night) for 60 days. Heat stress led to an increase in the plant height growth rate, fourth internode growth rate, and membrane damage, and due to heat-induced damage to chloroplasts, decrease in chlorophyll biosynthesis and photosynthetic efficiency. Three principal components were extracted by principal component analysis. Correlation and regression analysis showed that heat tolerance is positively correlated with the plant height growth rate, fourth internode growth rate, the content of chlorophyll b, photosynthetic rate, stomatal conductance, transpiration rate, tuber number, and tuber yield, and negatively correlated with the cell membrane injury level. The nine traits are accurate and representative indicators for evaluating potato tolerance to heat stress and could determine a relatively high mean forecast accuracy of 100.0% for the comprehensive evaluation value. Through cluster analysis and screening, cultivar FA, D73, and C132 had the highest heat comprehensive evaluation value, which could be further selected as heat-resistant varieties. This study provides insights into the different physiological mechanisms and accurate evaluation methods of potato cultivars under heat stress, which could be valuable for further research and breeding.

Comprehensive evaluation of morphological and physiological responses of seventeen Crassulaceae species to waterlogging and drainage under temperate monsoon climate

Unpredictable rainfall frequently results in excess moisture, which is detrimental to the landscape because it interferes with the genetic, morphological, and physiological processes of plants, even though the majority of urban landscapes frequently experience moisture shortages. A study was conducted to analyze the effects of a 36-day waterlogging phase and a subsequent 12-day recovery period on the morpho-physiological responses of 17 Crassulaceae species with the goal of identifying those which were more tolerant of the conditions. Results revealed that waterlogging stress has an impact on all morpho-physiological parameters. Sensitive materials (S7, Hylotelephium telephium ‘Purple Emperor’ and S15, S. sexangulare) showed severe ornamental quality damage, mortality, decreases in total dry biomass, root-shoot ratio, and chlorophyll content, as well as higher MDA concentrations. Lower reductions in these parameters, along with improved antioxidant enzyme activities and greater recovery capabilities after drainage, were observed in the most tolerant materials S2 (H. spectabile ‘Brilliant’), S3 (H. spectabile ‘Carl’), and S5 (H. telephium ‘Autumn Joy’). Furthermore, with the exception of early death materials (S7 and S15), all materials showed varying intensities of adventitious root formation in response to waterlogging. The 17 species were divided into 4 clusters based on the comprehensive evaluation value. The first group included S1-S3, S5-S6, S8-S12, which were waterlogged tolerant with the highest values (0.63–0.82). S14 belongs to the intermediate waterlogging tolerant. S4, S13, S16, and S17 were clustered into the low waterlogging-tolerant group. S7 and S15 were the most susceptible to waterlogging. The survival and success of Crassulaceae species (especially, the first and second cluster), throughout this prolonged period of waterlogging (36 days) and recovery were attributed to a combination of physiological and morphological responses, indicating that they are an appealing species for the creation of rain gardens or obstructed drainage locations.

The Coupling Effect of Traditional Culture and Rural Tourism Industry Development--The Case of Shandong Province Domain

Abstract While the rural tourism industry is developing and expanding rapidly, the structural adjustment and optimization of the traditional culture industry has also gradually attracted academic attention. This paper explores the deep integration of two industries based on the coupling mechanism of conventional culture and the development of rural tourism industries. Then, it constructs the index system of the development level of the traditional culture industry and rural tourism industry in Shandong, utilizes the entropy value method to calculate the weights of the indexes of the development level of the two industries, and adopts the linear weighting method to calculate the comprehensive evaluation value. Among them, “the development scale of the traditional culture industry (0.444)” and “the scale of the rural tourism industry (0.2918)” have the largest weight value. The comprehensive evaluation value of the development level of traditional culture and rural tourism industry of Shandong Province in 10 years rises respectively by Finally, by constructing the coupling degree model of conventional culture and rural tourism industry and the coupling coordination degree model, the stage-specific coupling evaluation of traditional culture and rural tourism industry is measured as 0.85&lt;C≤1, which indicates that the development level of conventional culture and rural tourism industry of Shandong Province is in the stage of high coupling. The study provides a decision-making reference for promoting the high-quality development of traditional culture and rural tourism industries, which has important theoretical and practical significance.

Research on clothing brand image design and communication under the SMCR communication model

Abstract This paper utilizes the SMCR communication model to explore factors influencing clothing brand communication. We develop an evaluation model that assesses the effectiveness of these brands’ image communication, providing essential insights for brand design and communication strategy selection. The model employs a fuzzy comprehensive evaluation method for setting assessment criteria and an Analytic Hierarchy Process (AHP) to calculate the weight distribution of each index, forming a fuzzy affiliation judgment matrix. An evaluation system was constructed and applied to Chinese apparel brands, revealing that interest circles, online fashion bloggers, and video and live broadcasting websites are the top channels, accounting for over 50% of total brand image communication. In terms of preference, IP co-branded models, national fashion products, and tournament models are in the top three, accounting for 10.35%, 9.73%, and 8.86%, respectively. The comprehensive evaluation value of brand image communication effect in China’s apparel industry is 5.63, with a rating of “poor,” and there is still much room for improvement in apparel brand design and communication.

Morphological and physiological responses of 14 macadamia rootstocks to drought stress and a comprehensive evaluation of drought resistance

Planting macadamia in karst rocky desertification areas has significant ecological and economic benefits. However, the production of macadamia in karst area are facing serious drought challenges due to the poor water holding capacity of soil, and less and uneven precipitation resulting with the rising of temperature in the world. Selecting and breeding drought-resistant rootstocks is one of the main measures to solve this problem in macadamia nut production in karst area. In this study, the morphological and physiological performance of half-sib family offspring of 14 macadamia cultivars were investigated under severe drought conditions and their drought resistance was evaluated comprehensively in order to select the most suitable macadamia rootstocks. Results revealed that the leaves of all rootstocks were damaged after drought stress. Among the tested rootstocks, the leaves of QA5 were damaged the most severely, and the leaves of the whole plants were chlorotic, followed by C11, A4, and NY2, while the symptoms in leaves of O.C and H2 was less obvious after drought stress for 30 d. At the same time, the O.C and H2 had the lowest drought injury index (DII), which was 0.40, while the QA5 had the highest DII value (0.82). The physiological indices, including malonaldehyde (MDA), superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX), soluble protein (SP), soluble sugar (SS), proline (Pro) and relative electrolyte leakage (REL) increased (Drought tolerance coefficient, DTC > 1) after drought stress, while relative water content of leaves (LRWC), chlorophyll (Chl), relative water content of roots (RRWC) and root activity (RA) decreased (DTC < 1) in all cultivars. Except SP, SS and Pro, there were significant differences in other indexes among different rootstocks. The 13 physiological indices were transformed into seven independent, comprehensive indices through principal component analysis (PCA), whose cumulative contribution ratio reached 82.905%. The 14 rootstocks were divided into 3 categories based on the comprehensive evaluation value (D). The O.C and H2 were clustered into the drought-tolerant group, whose D values were 0.743 and 0.669, respectively. The NY3, 333, 788, NY116, NY1, NY2 were belonged to the intermediate drought tolerance group, the D values were 0.530, 0.508, 0.497, 0.465, 0.457 and 0.441 respectively. Lastly, A16, A4, C11. GR1, 695 and QA5 were clustered into low drought tolerance group with the lowest D values of 0.396, 0.391, 0.360, 0.360, 0.355, 0.324, respectively. The result of stepwise regression analysis showed that MDA, POD, APX, SS, REL, RRWC and RA were the most significant physiological and biochemical indices that played a significant role in drought tolerance, and could be used as the primary evaluation and identification indicators of drought tolerance of macadamia. These results indicated that the drought-resistant rootstocks of macadamia could improve their drought resistance by stimulating the cluster root development, increasing antioxidant enzyme activity and raising the content of osmotic regulatory substances under drought stress. Our study not only provided alternative rootstocks for macadamia drought-resistant cultivation, but also provided the theoretical basis for the drought-resistant rootstock breeding of macadamia by expounding the physiological mechanism of drought-resistance.

Identification of forest fire points under clear sky conditions with Himawari-8 satellite data

ABSTRACT Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring.

The target selection and quantitative evaluation for deep geothermal resource zoning of typical geothermal fields in central Hebei of North China plain

With the rapid economic development in North China, the demand for geothermal energy is increasing. It is urgent to find favorable deep geothermal resource targets in North China. Although geothermal resources in the North China Plain are widely distributed, in order to develop deep geothermal resources in North China safely, stably and efficiently, it is essential to carry out the target selection and evaluation of geothermal resource zoning. This article takes the typical geothermal fields in the central Hebei region as the research object, and through the comprehensive collation of regional geothermal geological data, constructs an optimization evaluation indicator system for geothermal resource target areas from three aspects: resource conditions, mining potential, and heating demand. On this basis, it establishes a linear relationship between the attribute values and scores of each indicator, and uses the analytic hierarchy process to assign weights to each indicator and calculate the comprehensive weight. Then, the comprehensive evaluation value is obtained by weighted calculation of the scores and comprehensive weights of each block in the grid segmentation of the evaluation area. Finally, through the spatial analysis function of GIS, the comprehensive evaluation values of all blocks in the evaluation area were analyzed using kriging difference analysis, and a comprehensive evaluation map, the geothermal resource prospective target area map, was finally obtained. Using quantitative zoning evaluation methods, the target areas for exploration and development of deep geothermal resources in central Hebei Province have been delineated within a large region. The evaluation results indicate that the Cambrian-Ordovician reservoir target area is relatively large, and there are many favorable target areas with good reservoir conditions in the Middle-Upper Proterozoic. The excellent prospective target area of the Mesoproterozoic geothermal reservoir accounts for 56%, mainly located in the Xingji uplift and Gaoyang low uplift. Xiong’an New Area, Cangzhou, and the eastern part of Hengshui are excellent prospective target areas for the evaluation of the Mesoproterozoic geothermal reservoir in a single area. This evaluation method can provide a reference for the optimization of resource exploration and development target areas in key regions.