Comprehensive Evaluation Model Research Articles

Industrial technology network security measurement in international trade under discrete hopfield neural network

As global economic integration continues to advance, international trade has become increasingly vital for the economic development and growth of nations. This research aims to assess the trends in industrial technology security within China’s international trade and provide practical guidance for policy-making, corporate strategies, and international cooperation. The significance of the rising trend in security within China’s international trade industry lies in its establishment of a robust foundation for the long-term development of China’s international trade, contributing to its cooperation and competitiveness with other countries. In addressing the limitations of traditional measurement methods and providing a more comprehensive and accurate assessment of industrial technology security, this research presents an approach based on a discrete Hopfield Neural Network (HNN) for evaluating industrial technology security in international trade. This method integrates multiple indicators, including technology gap rates, to construct the Superior Quality Engineering (SQE) comprehensive evaluation model. The research employs a combination model of “entropy-grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-discrete HNN” to assess industrial technology security. This research evaluates international trade industry technology security using patent data from 2015 to 2022 as samples. The results indicate an overall upward trend in security in China’s international trade industry. Within this trend, the research observes a stepwise increase in scale components, leading to continuous improvement in security. In terms of quality components, although security develops relatively slowly overall, it exhibits a trend of initial gradual decline followed by rapid growth. Regarding efficiency components, there is overall slow growth with periodic fluctuations. This research outcome provides substantial support for the research of industrial technology in international trade. The proposed method can assist businesses in evaluating their technological security in international trade and offer robust support for international trade decision-making.

Comprehensive Evaluation of Decarbonization Technologies: A Case Study of Residential Buildings in Zhuzhou City, China

Efficient carbon emission reduction technologies in buildings are necessary for achieving the “Dual carbon” goal in China. In this study, a comprehensive evaluation model is proposed to assess the effect of carbon emission reduction based on the analytic hierarchy process–entropy weight–coefficient of variation model which takes newly built residential buildings in Zhuzhou City as the research object. The results show that the preferred materials for the roof and exterior walls of the building’s envelope structure were flame-retardant extruded polystyrene boards, and porous shale bricks were preferred as the main materials for the exterior walls. In addition, the rooftop solar photovoltaic system and energy-saving air conditioning technology were suitable in terms of being renewable and were better utilized. In the end, carbon emissions were significantly reduced when using the building decarbonization technologies. This study provides a new reference for choosing materials and technologies for the design of residential buildings in Hunan Province and even other regions with hot summers and cold winters.

Multi-criteria comparison analysis of spatial equity for emergency shelters in old and new urban districts: A case study in Wuhua District and Chenggong District of Kunming City, China

With the progression of urbanization and the concentration of populations in urban areas, cities are increasingly exposed to disaster risks. Emergency shelters play a critical role in disaster prevention and mitigation, as they are vital for providing temporary refuge and protection during and after catastrophic events. Assessing the spatial equity of these shelters is conducive to enhancing emergency relief capabilities and aiding in disaster risk reduction. However, there remains a lack of clarity regarding the comparative and quantitative disparities between new and old urban areas, particularly in developing countries like China, where urbanization is rapid and populations are dense. To address this issue, this study employed a combination of multi-source data and GIScience technology to analyze the spatial equity of emergency shelters in Wuhua District and Chenggong District of Kunming City, China. We evaluated the overall spatial equity of emergency shelters in both the old urban area (Wuhua District) and the new urban area (Chenggong District) using a comprehensive evaluation model with three criteria: evacuation population capacity, spatial accessibility, and the level of per capita effective shelter area. The results revealed that the old urban area suffers from insufficient effective emergency shelter capacity due to saturated construction land. More than 67 % of the shelters are short-term emergency shelters located close to buildings, which compromises spatial accessibility during earthquake disasters. The level of per capita effective shelter area, as indicated by SSTD, is 24.42 in Wuhua District, suggesting that the effective shelter area in each subdistrict is consistently low, regardless of its urban or suburban classification. In contrast, the new urban district boasts substantial effective emergency shelter capacities, both in the long-term and short-term types, surpassing the reference capacities. The rational spatial layout of various types of emergency shelters ensures that most regions with concentrated buildings are enveloped in accessibility time layers within a 25-min range. The SSTD index of Chenggong District is 349.23, underscoring the significant disparity in the level of per capita effective shelter area at the subdistrict scale, particularly in the district's urban villages and suburban regions. The findings of this study serve as a scientific reference for emergency management and urban planners to reasonably plan or upgrade shelter facilities, thereby improving urban resilience and promoting spatial equity in resource allocation among subdistricts, communities, and residents.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed “acoustic-thermal-stress” fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σmax. Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.

Comprehensive evaluation of coal burst risk using optimized linear weighted model

Comprehensive evaluation of coal burst risk using optimized linear weighted model

Rams-based comprehensive evaluation method for EMU components

EMU (Electric Multiple Units) components were evaluated in multiple dimensions, i.e. reliability, availability, maintainability, and safety. A parameter estimation method, which is based on the data on the mileage between failures and quantities monitored in the actual operation, was put forward for the Weibull proportional hazard model to determine their operational reliability. A multi-index component maintainability evaluation model, which is based on empirical distribution functions, and a maintainability cluster evaluation model, which is based on whitened weight functions, were built, and the entropy weight method was introduced to determine the estimates of the indexes. A component safety evaluation method, which has the safety risk factor as its evaluation basis, was built using both the fuzzy theory and the analytic hierarchy process. A RAMS comprehensive evaluation model based on the linear weighted synthesis method was proposed. All of the models and methods are aimed at meeting the needs of this multidimensional component evaluation. This evaluation method was verified using actual gearbox operation parameters. The reliability and availability evaluation curves based on the Weibull proportional hazard model, whose parameters were determined according to the failure data, show that the EMU component reliability and availability decrease as the EMU mileage increases, thus verifying that this evaluation method is reasonable. The calculated EMU gearbox degree of safety is 0.8659, at an excellent level. The evaluated EMU gearbox maintainability is at the moderate level of III. The proposed evaluation method can reflect the relations of the component reliability and availability to the quantities monitored in operation more accurately and more comprehensively and the actual component maintainability and safety levels in a more objective way. It can provide a theoretical basis for managers and design departments to improve and evaluate the performance of the components, determine their maintenance intervals, and develop scientific preventive maintenance strategies in order to reduce their overhaul cost and increase their maintenance efficiency.

A novel evaluation model of image registration for cone-beam computed tomography guided lung cancer radiotherapy.

The aim of the study was to establish a weighted comprehensive evaluation model (WCEM) of image registration for cone-beam computed tomography (CBCT) guided lung cancer radiotherapy that considers the geometric accuracy of gross target volume (GTV) and organs at risk (OARs), and assess the registration accuracy of different image registration methods to provide clinical references. The planning CT and CBCT images of 20 lung cancer patients were registered using diverse algorithms (bony and grayscale) and regions of interest (target, ipsilateral, and body). We compared the coverage ratio (CR) of the planning target volume (PTVCT) to GTVCBCT, as well as the dice similarity coefficient (DSC) of the GTV and OARs, considering the treatment position across various registration methods. Furthermore, we developed a mathematical model to assess registration results comprehensively. This model was evaluated and validated using CRFs across four automatic registration methods. The grayscale registration method, coupled with the registration of the ipsilateral structure, exhibited the highest level of automatic registration accuracy, the DSC were 0.87 ± 0.09 (GTV), 0.71 ± 0.09 (esophagus), 0.74 ± 0.09 (spinal cord), and 0.91 ± 0.05 (heart), respectively. Our proposed WCEM proved to be both practical and effective. The results clearly indicated that the grayscale registration method, when applied to the ipsilateral structure, achieved the highest CRF score. The average CRF scores, excellent rates, good rate and qualification rates were 58 ± 26, 40%, 75%, and 85%, respectively. This study successfully developed a clinically relevant weighted evaluation model for CBCT-guided lung cancer radiotherapy. Validation confirmed the grayscale method's optimal performance in ipsilateral structure registration.

Comprehensive Quality Evaluation Model of College Students Based on Deep Learning

Evaluating college students' quality in higher education institutions is significant for promoting enhancement and innovation in teaching and learning practices. Conventional evaluation strategies mainly depend on subjective evaluation, which limits their ability to capture the multifaceted characteristics of students. In this study, we developed a distinct deep learning (DL) architecture by combining the Convolutional Deep Belief Network and Red Fox Optimization (CDBN-RFO) to evaluate college students' quality. Initially, a database containing academic performance records, extracurricular activities, and other relevant information is collected and fed into the system. Then, the database was preprocessed to make it an appropriate format for subsequent analysis. Further, the proposed CDBN-RFO was created, and the CDBN was trained using the preprocessed database to understand the patterns and relations within data for evaluating college students' quality. Subsequently, the RFO approach was deployed to fine-tune the CDBN hyperparameters to their finest range, which increases the overall system performance. Subsequently, the RFO optimized the CDBN training process by fine-tuning its parameters to the optimal range, enhancing overall evaluation performance. The developed framework was modeled using Python software, and the results were analyzed, including accuracy, error, computational time, etc. Also, a comparative analysis was done with conventional algorithms, which validated that the proposed strategy outperformed them in accuracy.

Influence of the addition of Curdlan gel on the textural properties of beef emulsified sausage

The aim of this study was to investigate the application of Curdlan gel in beef emulsified sausage, to optimise its process parameters and to improve its textural properties in order to provide a safer and healthier way of processing meat products. Cure time, chopping time and steaming time were selected as the influencing factors to find out the optimal process combination by orthogonal test. Subsequently, the effects of different additive amounts of CY-1 and CY-2 Curdlan gel on the texture of beef emulsified sausage were investigated, and a comprehensive texture evaluation model was established. Through orthogonal tests, the optimal process combination was found to be 60 min for cure time, 20 s for chopping time, and 40 min for steaming time, and it was found that the additive amounts of CY-1 Curdlan gel of 1.5%~2.25% and CY-2 Curdlan gel of 0.75%~1.5% could improve the texture of beef emulsified sausage significantly, and produce the beef emulsified sausage with good texture characteristics. The production of beef emulsified sausage with good textural characteristics provides a certain reference for the optimization and improvement of the processing technology of beef emulsified sausage.

A performance evaluation method for energy storage systems adapted to new power system interaction requirements

In recent years, China’s new energy storage application on a large scale has shown a good development trend; a variety of energy storage technologies are widely used in renewable energy development, consumption, integrated intelligent energy systems, distribution grids, and microgrids; and substantial progress has been made in the research and development of technology and equipment, construction of demonstration projects, exploration of business models, and construction of standard systems. Up to now, a unified statistical index system and evaluation method standard for new energy storage has not yet been formed domestically or even internationally. The work takes the status quo of the new power system construction of the Hebei South Network as the research object and carries out research on the new energy storage statistical index system and evaluation method. It constructs a new energy storage power station statistical index system centered on five primary indexes: energy efficiency index, reliability index, regulation index, economic index, and environmental protection index; proposes Analytic Hierarchy Process (AHP)–coefficient of variation combination assignment method; and evaluates the development level of the new energy storage power station by adopting a comprehensive evaluation model based on the object element topology method. The new energy storage statistical index system and evaluation method are designed to provide a scientific index system and evaluation method for comprehensively monitoring, assessing and measuring the comprehensive performance and effect of new energy storage power plants in the process of operation and development, and optimizing the operation strategy of new energy storage power plants as well as the development and promotion of energy storage technology.

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

Has the poverty alleviation resettlement projects achieved just transition in Nujiang Prefecture of Southwest China?: A way to achieve SDGs

From 2016 to 2020, China successfully assisted 9.6 million people below poverty line, making a making a substantial contribution to both the UN-defined Sustainable Development Goals (SDGs) and the global effort to reduce poverty. The literature reveals a focus on resettlement justice concerning livelihood transformation, re-siting, and public participation. However, a dearth of scholarly perspectives exists regarding migrants' comprehensive satisfaction indicators of just transition, particularly on sustainable assessment. Therefore, the study regards migrants as “customers” of PAR projects and assesses the degree of realization of a just transition of resettlement through their satisfaction. A multilayered comprehensive evaluation model is being formulated using the survey data of 515 migrants residing in three county-level cities in Nujiang Prefecture of southwest China. The results show that there is an increase in satisfaction with the impact of PAR on the migrant households' income, material assets, housing conditions, infrastructure, natural environment, children's educational level and ethnic cultures. Moreover, satisfaction with migrants' employment stability, social networking level, and the succession of local customs decreased. The results help to show that the PAR project does affect various aspects of migrants' lives, as well as contributing to the SDGs of social, economic, and environmentally sustainable development. The construction of a sustainable assessment system oriented to the combination of different dimensions of migrants' satisfaction and importance can help reduce the inequalities that may arise from the project. Boosting migrants' overall satisfaction is the most significant factor to consider when developing robust policies to increase poverty alleviation achievements and promote sustainable livelihood development.

Evaluation of land resources carrying capacity based on entropy weight and cloud similarity

Land is the foundation of human life and development, which is also the most important part of a country. The study of land carrying capacity is one of the important contents of land management, wherein the evaluation of land resource carrying capacity (LRCC) is an important reference for land resource planning. Aiming at the information fuzziness and uncertainty in the evaluation of LRCC, firstly, a comprehensive evaluation model based on entropy weight and normal cloud similarity was proposed, which is based on cloud model theory and combined with normal cloud similarity measurement method and entropy weight method. Secondly, taking the asphalt pavement experiment as an example for empirical analysis, the experimental results are consistent with the actual situation, which proves the feasibility and effectiveness of the proposed model. Finally, taking China’s Chongqing city as the research area, the proposed evaluation model is used to study LRCC. The research results indicate that the comprehensive carrying capacity and average carrying capacity of various systems in China’s Chongqing have been improved in the past decade. Among them, the comprehensive carrying capacity rose from the second level during the "12th Five-Year Plan" period to the third level during the "13th Five-Year Plan" period. In the future, it is necessary to focus on the improvement of soil and water resources system and economic and technological system. This conclusion reflects LRCC of Chongqing in China objectively and has a reference value for Chongqing's land planning.

Study on Supply Shain Financing Scheme of A Vietnamese Coffee Company in China

This paper takes G7 Coffee, a Vietnamese coffee company, as an example to design a supply chain financing scheme suitable for the company. First of all, on the basis of explaining the concept of supply chain, supply chain financing and other related concepts, supply chain management theory, supply chain financing model, fuzzy comprehensive evaluation model and other theoretical foundations, based on the financing situation of a Vietnamese coffee company G7 coffee in China, combined with the financing problems of the company and the company's capital needs, The feasibility of supply chain financing of G7 Coffee in China for a Vietnamese coffee company is proposed. Secondly, this paper introduces the evaluation method of supply chain financing scheme of G7 Coffee in China of a Vietnamese coffee company. Finally, on the basis of clear objectives and principles of financing scheme design, an evaluation system of supply chain financing mode of a Vietnamese coffee company based on AHP is constructed. The applicability of accounts receivable supply chain financing, inventory supply chain financing and prepayment supply chain financing adopted by the company is further analyzed by fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation results show that The accounts receivable financing model is the best financing model for the supply chain financing of G7 Coffee in China of a Vietnamese coffee company. In order to ensure the smooth implementation of the supply chain financing plan of a Vietnamese coffee company G7 Coffee in China, this paper proposes to strengthen the guarantee from three aspects: financial system, credit management and risk control.

Evaluation of Air Traffic Control Ability Based on Fuzzy Theory and Multiple Evaluation Model

This paper constructs the structural model of the system of air traffic controllers' comprehensive ability evaluation. The use of analytic hierarchy process to determine the weight of various indicators should have weight values. The construction of the controller comprehensive capacity assessment model, fuzzy comprehensive evaluation method can be used to weigh matrix and membership matrix corresponding transformation, and then determine the comprehensive capacity of the controller to evaluate the value. In the assessment process, the advantages of various methods are combined, enabling the comprehensive evaluation of the comprehensive capacity of the controller, making the results tend to be reasonable. The research shows that the evaluation results made by this method can provide decision-making basis for aviation safety management at all levels. Using the multiple evaluation model, the controller’s comprehensive ability evaluation model is established to evaluate the comprehensive ability of the controller.

Dynamic monitoring and restorability evaluation of alpine wetland in the eastern edge of Qinghai–Tibet Plateau

Accurate monitoring of alpine wetland areas and scientific evaluation of alpine wetland restorability play important roles in revealing the changing mechanism of the ecological environment. However, because of the complexity and lack of spatial data, few studies on evaluation index system of restorability of alpine wetland considered wild animal habitat, such as habitat suitability index of black necked crane. Based on Landsat images of seven periods from 1990 to 2020, this study adopts the methods of spectral mixture analysis and change vector analysis to quantitatively demonstrate the variations and distribution patterns of alpine wetland ecosystem in the eastern edge of Qinghai–Tibet Plateau. The study focuses on determining the abundance of water, vegetation and soil, which the interannual variations represent as the shapes of ‘√’, mirror ‘N’ and normal ‘N’, respectively. In terms of natural ecological condition and human activity interference, we construct a comprehensive evaluation model of restorability of alpine wetland, separate the restorability district and construct the Bayesian network model to discuss the causal relationship between the restorability value and evaluation factor. Results show that the regions of alpine wetland in the study area changed most drastically from 1995 to 2000 and from 2000 to 2005. In addition, the total areas classified as suitable, relatively suitable and suitable for restoration account for 63.69 % of the study area, with a large potential for wetland restorability. Moreover, the prioritisation scheme of wetland restoration action includes the valley swamps of Heihe River in the middle, followed by the flat wetlands and grasslands in the south and lastly by uneven wetlands in the north.

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

Dietary diversity and healthy weight management in the population: a data-driven analytical approach

Cardiovascular and cerebrovascular diseases (CVDs), diabetes mellitus, malignant tumours and chronic obstructive pulmonary disease (COPD), as typical chronic non-communicable diseases (NCDs), have increasingly become the major factors threatening the health of Chinese residents. In view of this, this study aimed to deeply analyse the key indicators and their interrelationships affecting the health status of the population, using the eight dietary balance criteria proposed in the newly revised Dietary Guidelines for Chinese Residents of the Chinese Society of Nutrition to screen and process the relevant health indicators. By constructing a comprehensive evaluation model and visualising the data, this study reveals the irrational factors in residents' dietary habits, such as high-fat diet and excessive alcohol consumption, which pose potential risks to residents' health. In addition, this paper explores the relationship between residents' dietary habits, exercise frequency and healthy body weight, aiming to provide a scientific basis and practical guidance for improving public health.

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.