Development Of Evaluation Model Research Articles

A Deep Learning-based Model for Evaluating the Sustainability Performance of Accounting Firms

The harmonious and stable development of society is strongly related to the sustainable development of enterprises. In order to better face the challenges of environmental resources, sustainable development must be included in the development focus of accounting enterprises. The research proposes a performance evaluation model based on deep learning, improves RBMs model on the basis of deep belief network (DBN), improves the accuracy of the model through reverse fine-tuning technology, and effectively combines multiple restricted Boltzmann machines (RBMs) and Softmax classifiers to build a modular multi classification model to complete the sustainable development performance evaluation of accounting enterprises. The performance of RBM fine tuning classifier is higher than that of RBM expression and PCA (Principal Component Analysis) expression, which mainly shows the effectiveness and stability of feature extraction. The network output results of test samples are converted into prediction performance evaluation. The model is evaluated by average precision (AP), average recall (AR), and prediction accuracy. The AP, AR, and prediction accuracy of the proposed method are 86.95%, 89.74%, and 88.29% respectively, which are higher than Softmax classifiers, Back Propagation (BP) neural networks, and DBN based Softmax methods, It shows that this method is superior to other algorithms in the application of performance evaluation model for sustainable development of accounting enterprises, and it is feasible and effective, which is of great significance to the establishment of performance evaluation model for the accounting industry.

Development of digital handout of quantum mechanics no spin and quantum mechanics with spin using professional pdf flip app

This study aims to produce a digital handout on the subject of mechanics quantum mechanics without spins and quantum mechanics with spins are valid and practical. Development This digital handout uses a combination of Rowntree’s development model and evaluation model Tessmer formative. The Rowntree development model consists of three stages, namely: planning, development and evaluation. The evaluation stage consists of four stages, namely: self evaluation, expert review, one-to-one evaluation, small group evaluation. Technique data collection in this study using walkthroughs and questionnaires. Research subject This is a student who has taken the introductory quantum physics class of class 2017 Indralaya class for the Physics Education Study Program, Sriwijaya University. Obtained the average scorethe average that meets the very valid category with a score on the content aspect of 4.92, the design aspect 4.46, and the language aspect 4.5. And meet the very practical category with an average score of 4.43at the one-to-one evaluation stage and 4.52 at the small group evaluation stage.

A Conceptual User Experience Evaluation Model on Online Systems

An online system has become a priority for organisations or companies in many countries, as it allows many processes to be conducted via online platforms, which contributes to profit gain. There are different types of user experience (UX) evaluation models that have been proposed to guide the measurement and development process. However, most of these models only have dimensions, and there is no guidance for UX measurement on online systems. The lack of evaluation models for online system measurement requires further investigation. This paper aims to identify the gaps in UX evaluation models, and develop a conceptual UX evaluation model for online systems. The method used in this study includes reviewing several literatures and shortlisting the relevant publications on UX and online systems. After that, the gaps were identified from the existing UX evaluation model in the relevant publications based on the ISO standard. Then, the study identified the important components of UX, and proposed a new conceptual UX evaluation model for online systems. The results of the study are the identification of the gaps in existing UX evaluation models, and the development of a new conceptual UX evaluation model that is specifically for online systems. Therefore, the results help in considering UX dimensions, criteria, and metrics and potential UX components for evaluation and measurement. The paper contributes to system developers, designers, and also researchers for future UX evaluation model development for online systems. Future studies could use the reviewed UX evaluation models to identify relevant dimensions of online systems, and hence improve the model that they will develop. The findings may also be beneficial to organisations that own online systems by providing guidelines on important dimensions involved in their UX-based evaluations.

Research on Measurement and Application of China’s Regional Logistics Development Level under Low Carbon Environment

To solve the problem of fuzziness and randomness in regional logistics decarbonization evaluation and accurately assess regional logistics decarbonization development, an evaluation model of regional logistics decarbonization development is established. First, the evaluation index of regional logistics decarbonization development is constructed from three dimensions: low-carbon logistics environment support, low-carbon logistics strength and low-carbon logistics potential. Second, the evaluation indexes are used as cloud model variables, and the cloud numerical characteristic values and cloud affiliation degrees are determined according to the cloud model theory. The entropy weight method is used to determine the index weights, and the comprehensive determination degree of the research object affiliated to the logistics decarbonization level is calculated comprehensively. Finally, Beijing-Tianjin-Hebei region is used as an example for empirical evidence, analyzing the development logistics decarbonization and its and temporal variability in Beijing, Tianjin and Hebei provinces and cities. The results of the study show that the development logistics decarbonization in Beijing, Tianjin and Hebei Province has been improved to different degrees during 2013–2019, but the development is uneven. Developing to 2019, the three provinces and cities of Beijing, Tianjin and Hebei still have significant differences in terms of economic environment, logistics industry scale, logistics industry inputs and outputs, and technical support.

Application of Risk-Informed Validation Framework to a Flooding Scenario

In recent years, the use of advanced simulation tools for modeling the behavior of flooding at a nuclear power plant has gained significant importance. The credibility of advanced simulation codes is assessed using formal approaches for verification and validation. In this manuscript, a formal risk-informed validation approach is proposed that provides a basis to quantify credibility of system-level risk assessments. The credibility of system-level validation is represented using a probabilistic metric and maturity levels. The novelty of the proposed approach lies in the integration of risk-informed validation approach with the US Nuclear Regulatory Commission’s (USNRC) Evaluation Model Development and Assessment Process (EMDAP) framework. This allows transformation of EMDAP into a risk-informed EMDAP. The applicability of the proposed framework is evaluated by application to a flooding scenario of a sunny-day dam failure. Application to the flooding scenario illustrates that the credibility of flooding simulations can be assessed using formal quantification, which is otherwise not possible in the existing EMDAP framework.

Artificial Intelligence Assists the Construction of Quantitative Model for the High-Quality Development of Modern Enterprises.

Artificial intelligence companies are different from traditional labor-intensive and capital-intensive companies in that their core competitiveness lies in technology, knowledge, and manpower. Enterprises show the characteristics of a high proportion of intangible assets, strong profitability, and rapid growth. At the same time, there are also the characteristics of high risk and high uncertainty. In addition to the existing value brought by existing profitability, corporate value should also consider the potential value brought by potential profitability. Enterprise value is affected by many factors such as profitability, growth ability, innovation ability, and external environment. Traditional valuation techniques are often utilised to value artificial intelligence businesses in the present market. Traditional valuation methods ignore the dynamics and uncertainties of artificial intelligence enterprise value evaluation, make static and single predictions of future earnings, ignore the value of enterprise management flexibility, and are unable to assess the intrinsic value of artificial intelligence businesses. Based on the projection pursuit method, this paper constructs a modern high-quality development enterprise high-quality development evaluation model, uses real-code accelerated genetic algorithm to optimize the projection objective function, and calculates the best projection direction vector and projection value. The collected sample data can be imported into the evaluation model to calculate the comprehensive evaluation value of the high-quality development of modern high-quality development enterprises and the weights of various indicators included. By comparing the size of the comprehensive evaluation value, each sample can be calculated Evaluation of the level of high-quality development. The results show that the high-quality development level of China's overall economy is on the rise, but the level of development is still low, and there is a large gap between the development level of the eastern region and the central and western regions. Using the systematic generalized moment estimation method, empirically, we analyse the impact of artificial intelligence on the high-quality economic development. The results show that artificial intelligence at the national level and in the central and western regions will significantly promote high-quality economic development, while artificial intelligence in the eastern region has a significant inhibitory effect on high-quality economic development.

Constructing an Evaluation Index System for China’s Low-Carbon Tourism Region—An Example from the Daxinganling Region

With the intensification of global warming, the development of low-carbon tourism is not only an inevitable requirement for human development, but also an important way to enhance tourism, improve the quality of tourism, and obtain greater economic and social benefits from tourism. Scientifically and effectively constructing a low-carbon tourism development level evaluation index system is conducive to the healthy and sustainable development of the tourism industry. However, the current evaluation of the development level of low-carbon tourism mainly focuses on low-carbon development, energy conservation, and emission reduction. There are few studies and a lack of specific empirical research evaluating the development of low-carbon tourism. In this research, the Delphi method and analytic hierarchy process were used for data collection and corresponding weight analysis. This research refers to the results of existing studies on low-carbon tourism evaluation index systems, and constructs an objective and quantitative evaluation model. It establishes a low-carbon tourism development evaluation model with 34 specific indicators from the three dimensions of the tourism economic support level, the tourism low-carbon development level, and the tourism policy support level. The research results were applied to the Daxinganling region for verification. The results show that the development of tourism in the Daxinganling region has changed from a mode dominated by economic interests to a mode in which tourism-related parties begin to seek effective low-carbon methods to maintain low-carbon tourism activities. In addition, the Daxinganling region has the lowest level of low-carbon tourism development and the highest level of financial support. Based on the research conclusions, corresponding suggestions for the development of low-carbon tourism in the Daxinganling region are provided. The results are also intended to provide a reference point in the optimization of low-carbon tourism development models in this and other regions.

Evaluation Model for Sustainable Development of Settlement System

Sustainability of settlement systems is of greatest relevance in political and socio-economic stability all over the world. The development effectiveness of a rural settlement system involves the solution of a number of matters in sustainable development, namely social welfare and environmental balance, economy and industry development, improving the pipeline and utility infrastructure, and improving the efficiency of the decision-making process. Currently, the sustainability of a rural settlement system is one of the key objectives in regional planning in post-Soviet countries. The introduction of new tools for assessing and managing the settlement system development is particularly true for Belarus, as a country with a strong focus on agricultural industry. The research aim was to develop and approve a model for evaluating the settlement system development. The research methods were based on the complex and interdisciplinary approaches, namely the system-element approach, the comparative analysis, spatial and mathematical modelling, factor analysis, and the cartographic analysis. The model was approved by practical consideration for evaluating the development of the analogue object at the local planning level. The practical relevance of the research is associated with the potential for using the model as a significant tool in land use planning. The model employs both quantitative and qualitative evaluation to obtain alternative solutions towards sustainable development of rural areas. Another advantage of the model is its multifunctionality, which enables: (1) sustainability evaluation of a settlement system, (2) establishment of regional planning priority areas, and (3) development of specific measures for ensuring the sustainability of a regional settlement system and its elements.

Research on the Evaluation Index System of Teachers’ Training Quality in Higher Vocational Colleges

In order to improve teachers’ comprehensive ability and quality, it is extremely important to conduct training for teachers. When higher vocational colleges train teachers, the construction of an evaluation index system for the training quality is important to determine the training effect, point out the training direction, and deepen the training content, thus laying the foundation for systematic and reasonable trainings. Based on Kirkpatrick model and teachers’ professional development evaluation model, this article expounds the specific means of the evaluation index system of teachers’ training quality from six first-class indicators in order to lay the foundation for the improvement of teachers’ training quality.

Stochastic techno-economic evaluation model for biomass supply chain: A biomass gasification case study with supply chain uncertainties

Sustainable development has been a goal for many industries, which can be achieved via the substitution of non-renewable feedstocks with greener alternatives such as substituting coal with biomass for gasification process. The sustainably produced biomass-derived syngas has a lower carbon emission when utilized as a fuel. Although most studies have reported the possible impact of biomass quality on the feasibility of biomass conversion process, there is still little published works on the incorporation of biomass quality directly into developed mathematical evaluation model. Therefore, this paper aims to perform techno-economic feasibility evaluation on biomass gasification process, with the consideration of various supply chain uncertainties via Monte Carlo simulation (i.e., biomass supply, biomass quality, biomass pricing, transportation fuel price, and syngas sale price). This is achieved via integration of a generic correlation equation relating biomass quality to specific syngas yield into the Monte Carlo model. Upon consideration of the uncertainties, this study revealed that mesocarp fibre has a higher net present value (NPV) in the range of Malaysian Ringgit (MYR) 70 million to greater than MYR 120 million amongst the three types of palm-based biomass (empty fruit bunches (EFB), palm kernel shells (PKS), and mesocarp fibre). Additionally, environmental assessment performed deduced that mesocarp fibre is slightly more preferable in the impact category for HTPI and TTP with a probability of 98 % to achieve the equivalent emissions within the lower range of 0–0.2 kg 1,4-C6H4Cl2,eq whereas EFB and PKS has a probability of 18 % and 28 %, respectively.

Analysis of determinants of work performance for seafarers based on fuzzy Electre model

Nowadays, most shipping companies are aware of the importance of analysing seafarers’ work performance. Thus, improving seafarers’ work performance or finding out the ways by which seafarers’ work performance can be enhanced is becoming one of the decisive factors for the success of any shipping company. Any organization aims to have employees who can accomplish corporate goals and objectives using available resources efficiently and effectively. Manpower performance can be enhanced by putting more emphasis on factors that can stimulate employees’ motivational level, creativity and job satisfaction. In this study, we use literature review and experts’ opinions to identify determinants of workplace performance for seafarers working with Marine Services Company Limited (MSCL), a shipping company owned by the government of Tanzania. The data is collected from a random sample of 30 seafarers working for the MSCL and a Fuzzy Electre Model (FEM) is developed and applied to determine the significance of the factors and sub-factors for the work performance of seafarers. The fuzzy terms are adopted in order to integrate the uncertainty of subjective judgment when evaluating alternatives. The computational results validate the effectiveness of the developed evaluation model. This study is resourceful for the top management of MSCL, as it points out those factors that can directly affect the performance of her seafarers. Seafarers are important assets of any shipping company and thus, this study helps shipping enterprises devise efficient human resource strategies to get maximum benefit from this kind of assets. More importantly, enhanced seafarers’ work performance leads to improved company’s productivity and higher service quality and therefore make the shipping company competitive in the trading markets.

A Grey Wolf Optimization-Based Method for Segmentation and Evaluation of Scaling in Reinforced Concrete Bridges

Bridges are prone to severe deterioration agents which promote their degradation over the course of their lifetime. Furthermore, maintenance budgets are being trimmed. This state of circumstances entails the development of a computer vision-based method for the condition assessment of bridge elements in an attempt to circumvent the drawbacks of visual inspection-based models. Scaling is progressive local flaking or loss in the surface portion of concrete that affects the functional and structural integrity of reinforced concrete bridges. As such, this research study proposes a self-adaptive three-tier method for the automated detection and assessment of scaling severity levels in reinforced concrete bridges. The first tier relies on the integration of cross entropy function and grey wolf optimization (GWO) algorithm for the segmentation of scaling pixels. The second tier is designated for the autonomous interpretation of scaling area. In this model, a hybrid feature extraction algorithm is proposed based on the fusion of singular value decomposition and discrete wavelet transform for the efficient and robust extraction of the most dominant features in scaling images. Then an integration of Elman neural network and GWO algorithm is proposed for the sake of improving the prediction accuracies of scaling area though optimization of both structure and parameters of Elman neural network. The third tier aims at establishing a unified scaling severity index to assess the extent of severities of scaling according to its area and depth. The developed method is validated through multi-layered comparative analysis that involved performance evaluation comparisons, statistical comparisons and box plots. Results demonstrated that the developed scaling detection model significantly outperformed a set of widely-utilized classical segmentation models achieving mean squared error, mean absolute error, peak signal to noise ratio and cross entropy of 0.175, 0.407, 55.754 and 26011.019, respectively. With regards to the developed scaling evaluation model, it accomplished remarkable better and more robust performance that other meta-heuristic-based Elman neural network models and conventional prediction models. In this context, it obtained mean absolute percentage error, root-mean squared error and mean absolute error 1.513%, 29.836 and 12.066, respectively, as per split validation. It is anticipated that the developed integrated computer vision-based method could serve as the basis of automated, reliable and cost-effective inspection platform of reinforced concrete bridges which can assist departments of transportation in taking effective preventive maintenance and rehabilitation actions.

Probabilistic Linguistic Three-Way Multi-Attibute Decision Making for Hidden Property Evaluation of Judgment Debtor

Most law enforcement cases executed by the courts in China have behaviours of evading, evading, or even violently resisting execution or passively waiting for enforcement, which seriously affects the authority of legal judgments and the judiciary’s credibility. Therefore, we develop a hidden property evaluation model based on the probabilistic linguistic three-way multiattribute decision-making (PL3W-MADM) method. Considering the advantages of probabilistic linguistic term sets (PLTSs) expressing the evaluation information and their probabilities on judgment debtor given by expert judges, we extend the three-way decision method to a probabilistic linguistic environment and develop the strict PL3W-MADM model and flexible PL3W-MADM model. Then, the PL3W-MADM models are used to construct the hidden property evaluation model of judgment debtors. Finally, the developed hidden property evaluation model can quickly and effectively classify the judgment debtors into three categories: hidden behaviour, no hidden behaviour or lack of information, and temporary inability to judge. The results show that the developed model is more suitable for hidden property evaluation than the strict PL3W-MADM model and the flexible PL3W-MADM model.

Fuzzy evaluation of coordinated development of logistics and trade in coastal areas based on granger causal model

The Belt and Road is the logistics and trade of China’s coastal areas, which has gained new development opportunities. To explore the Belt and Road strategy in the current situation of logistics and trade development in coastal areas, the coastal logistics and trade measurement indicators were selected, an evaluation model of the collaborative development of logistics and trade was built by using time-series data and fuzzy theory, and the causal relationship between the indicators was analyzed through the Granger causal model. Results show that from 2009 to 2018, the total logistics value and trade volume of the studied area show an increasing trend year by year, and the trade scale shows an upward trend year by year. The trade dependence of the coastal area reaches about 37.5%, and the key driving force of economic development is trade export. The total logistics value of the coastal area and the trade import, export, and total import and export of the region, there is a high positive correlation among the three indicators, and the correlation coefficient between the import volume and the export value is more than 0.84, reaching the “extra high” evaluation level, and there is a highly significant positive correlation between the two indicators. The logistics and trade in the region have a positive correlation and collaborative development relationship.

Economic evaluation of rock oil project based on AHP algorithm

In order to reveal the characteristics of oil shale, the distribution of oil shale and the potential of resources in Jianchang Basin. The selection evaluation model and index system of oil shale development are established in this process. The weight of each layer evaluation index is determined, and a unified scoring system is established to obtain the index value. The weighted summation obtains the comprehensive score of each target area. The results show that the most important factors affecting the assessment of oil shale development are the geological and resource conditions of oil shale ore spots and the key parameters are oil shale oil content, ore body depth, and shale oil resources. By using AHP to evaluate 5 oil shale exploration areas in Jianchang Basin, it can be determined that Daqingshan and Chaoyang Qidaoquanzi exploration areas in Aohan Banner have the highest score, the best exploration prospect, the stable profitability, and the strong profitability of the project. Strong ability to resist risks.

Corporate Social Responsibility Based on Radial Basis Function Neural Network Evaluation Model of Low-Carbon Circular Economy Coupled Development

Under the background that the development of low-carbon circular economy is the objective requirement for the in-depth implementation of scientific development and the inevitable choice for promoting the sustainable development of economy and society, it is not only the requirement of corporate social responsibility but also the path to realize corporate social responsibility. Enterprises should become the representative and model of social responsibility practice in the development of low-carbon circular economy, in order to promote the fulfilment and development of corporate social responsibility in the whole society. Therefore, it is of great theoretical and practical significance to study the realization of corporate social responsibility in the context of low-carbon circular economy. This paper introduces the connotation of low-carbon circular economy and corporate social responsibility, analyses the reality and theoretical basis of realizing corporate social responsibility in low-carbon circular economy, analyses the interactive relationship between the development of low-carbon circular economy and the realization of corporate social responsibility, and puts forward the construction of enterprise low-carbon operation mechanism. This paper uses the research of corporate social responsibility based on radial basis function neural network to build a low-carbon circular economy. The evaluation model of environment economy coupling development is verified by an example, which provides useful guidance for the evaluation and development of corporate social responsibility.

Short-Packet Communications in Wireless-Powered Cognitive IoT Networks: Performance Analysis and Deep Learning Evaluation

In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems.

Research on Accounting Intelligence System Modeling of Financial Performance Evaluation

In the current competitive market environment, both enterprises and academia attach great importance to the research of financial performance evaluation. The quality of financial performance directly affects the sustainable development of enterprises. With the deepening of enterprise management concept, enterprises pay more attention to the use of financial performance evaluation analysis to promote the sound development of the whole enterprise. In order to understand the development status and development trend of enterprises and improve the efficiency of enterprise management, it is of great significance to establish a scientific and professional financial performance evaluation model for the sustainable development of enterprises. In this paper, based on FCM clustering algorithm, a fuzzy decision model is established. Combined with the factors affecting the financial performance of enterprises, the corresponding indicators are selected from the four aspects of profitability, operation ability, debt paying ability, and development ability to construct the financial performance evaluation system, and the comprehensive fuzzy evaluation model is constructed according to the financial evaluation system. This paper selects listed companies as the research object to evaluate and analyze the performance of enterprises. The results show that the overall performance of listed companies is not ideal and solvency, operation ability, and development ability need to be enhanced. Finally, in view of the problems existing in the listed enterprises, this paper puts forward the following countermeasures: implement cost assessment and pay attention to cost analysis to strengthen cost control; strengthen the cooperation between industrial chain enterprises and R&D departments; and pay attention to the use of talents and improve the ability of independent innovation.

Evaluation Model of Low-Carbon Circular Economy Coupling Development in Forest Area Based on Radial Basis Neural Network

In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. After an in-depth analysis of the particle swarm algorithm, an improved particle swarm algorithm is proposed to improve the search accuracy and capability of the algorithm by nonlinearly adjusting the inertia weights and introducing the average extreme value factor, in response to the problems of premature convergence and poor search capability that appear in the particle swarm algorithm. Through the analysis and evaluation of the interaction between industrial ecosystem and carbon emission, the main influencing factors of carbon emission are identified, and the size and magnitude of the influence of economic growth, industrial structure, energy intensity, and energy structure on carbon emission are determined; the current situation of the industrial ecological structure is evaluated, and the direction of optimization and adjustment of industrial economic structure, energy structure, and ecological structure is clarified. We construct a multidimensional multiconstraint multimodel industrial ecological structure optimization prediction model, set the development scenarios of economy and society, and optimize the prediction of low-carbon industrial ecological structure in forest areas; based on the simulation analysis of the prediction results, we propose the direction of industrial ecological structure adjustment and the path of industrial ecological system construction.

Use of Decision Trees to Evaluate the Impact of a Holistic Music Educational Approach on Children with Special Needs

In this study, decision trees were used to develop a pre-assessment model to help ascertain the impact of music education on children with special needs. The focus of the study was the application of an educational curriculum for 16 weeks, five sessions of 40 min duration per week, using the Holistic Music Educational Approach for Young Children (HMEAYC). The pilot program was implemented with children with special needs to measure its learning effectiveness. The methodology proved a better indicator for improved learning and a better measure of learning effectiveness. Statistical tests confirmed significant improvements in the values of the learning evaluation indices measured by HMEAYC after its implementation in children with special needs, supporting the positive effect of the implementation of HMEAYC for Taiwan’s special needs young children. For children with better learning results, the accuracy of the decision tree model was 84.0% for in-sample and the sensitivity equaled 98.0%. The results support the future development of evaluation models through machine learning languages, pre-assessment of the effectiveness of the implementation of HMEAYC, and the use of continuous investment in educational resources to improve the efficiency of special early childhood education in resource consumption for sustainable development.