Evaluation Model Research Articles

Spontaneous combustion coal gangue-based composite cement: Compressive performance and environmental benefits under grinding kinetics control

This study aims to tackle the environmental pressure of spontaneous combustion coal gangue by using its active silica-alumina content to create eco-friendly composite cement through a low-energy process. The Divas-Aliavden grinding kinetics equation was established, and powder characteristics were analyzed using particle size distribution, the Rosin-Rammler-Bennett model, and fractal dimension theory. The compressive performance of the coal gangue-based composite cement was tested. A comprehensive evaluation model linked grinding time, powder characteristics, compressive performance, and energy consumption, while also analyzing hydration products and environmental impact. Results showed that initial grinding stages had larger particle sizes and higher energy utilization efficiency. As grinding progressed, energy consumption increased. A grinding time of 35 min achieved optimal balance between energy efficiency and compressive performance, improving particle distribution uniformity and complexity. The compressive performance of the composite cement reached 95 % of ordinary Portland cement after 28 days of curing. The evaluation model indicated that specific surface area, d50, and fractal dimension are key factors influencing energy efficiency, determining energy consumption and particle distribution during grinding. Environmental assessment revealed that substituting 30 % of cement with coal gangue could reduce CO2 emissions by 250 kg CO2/t, achieving a reduction rate of 29.5 %. This study supports the large-scale application of spontaneous combustion coal gangue, contributing to efficient resource utilization and environmental protection.

Enhancing Global Poliomyelitis Surveillance Performance is Crucial for Eradication Efforts

Background: Poliomyelitis remains a significant public health challenge, with global efforts focused on its eradication. One critical strategy in this endeavor is the implementation of an acute flaccid paralysis (AFP) surveillance system, which is essential for early detection and response to poliovirus transmission. Specific Background: Despite the system's importance, there is limited evaluation of its performance, particularly in regions like Diyala province, Iraq. Knowledge Gap: Previous studies have not adequately assessed the AFP surveillance performance indicators specific to this region, necessitating a comprehensive analysis. Aims: This study aimed to evaluate the performance indicators of the AFP surveillance system in Diyala province during 2021, utilizing established World Health Organization (WHO) standards. Results: The cross-sectional study analyzed data from seven primary health care centers. Key findings revealed a Non-Polio Acute Flaccid Paralysis rate of 5.11 per 100,000 children under 15 years. Performance indicators included 100% reporting completeness, 95% timeliness of notification and investigation, and 97% stool quality for laboratory analysis. Furthermore, 97.3% of specimens reached the national laboratory within three days, demonstrating high operational efficiency. Novelty: This study is one of the first to provide a detailed evaluation of the AFP surveillance system in Diyala province, highlighting areas of strength and compliance with WHO targets. Implications: The findings underscore the effectiveness of the AFP surveillance system in this region, suggesting that its continued implementation can significantly contribute to the global eradication efforts of poliomyelitis while also serving as a model for similar evaluations in other regions. Highlights: High reporting completeness and timeliness in AFP surveillance. Non-Polio AFP rate exceeds WHO targets, indicating effective monitoring. Study supports global efforts toward poliomyelitis eradication. Keywords: Poliomyelitis, Acute Flaccid Paralysis, Surveillance System, Performance Indicators, Iraq

A Study on Energy Production and Consumption Based on Seasonal ARIMA and REAO

In the face of the global energy challenge, this study delves into the intricate landscape of energy dynamics within the rapidly evolving global economy and technological sphere. Recognizing energy as a pivotal driver of societal progress, we present a comprehensive evaluation and optimization model for China's energy sources, emphasizing economic, cost, electric energy, and environmental considerations. Employing time series models, specifically ARIMA and gray scale forecasting, we meticulously analyze wind and solar power generation, as well as overall power consumption. The results showcase nuclear power and hydropower as significant contributors to the energy landscape. Leveraging a linear programming model, we simulate and optimize the future energy development structure, considering constraints such as cost, energy, capacity, and carbon emissions. Our findings reveal the optimal quota contributions of thermal power, hydro power, nuclear power, wind power, and solar power as 13.7%, 14.2%, 17.6%, 54.5%, and 0%. Building upon these insights, we propose strategic recommendations for sustainable electric energy development. Emphasizing wind and solar energy's potential, we advocate for continued interest and increased support for technological advancements. Additionally, we call for the establishment of comprehensive power and energy development plans, integrating economic and environmental goals. To address imperfections in China's electricity tariff system, we recommend ongoing reforms aligned with market dynamics and environmental considerations.

Improved Fracture Permeability Evaluation Model for Granite Reservoirs in Marine Environments: A Case Study from the South China Sea

Permeability is a crucial parameter in the exploration and development of oil and gas reservoirs, particularly in unconventional ones, where fractures significantly influence storage capacity and fluid flow. This study investigates the fracture permeability of granite reservoirs in the South China Sea, introducing an enhanced evaluation model for planar fracture permeability based on Darcy’s law and Poiseuille’s law. The model incorporates factors such as fracture heterogeneity, tortuosity, angle, and aperture to improve permeability assessments. Building on a single-fracture model, this research integrates mass transfer equations and trigonometric functions to assess intersecting fractures’ permeability. Numerical simulations explore how tortuosity, angle, and aperture affect individual fracture permeability and the influence of relative positioning in intersecting fractures. The model makes key assumptions, including minimal consideration of horizontal stress and the assumption of unidirectional laminar flow in cross-fractures. Granite outcrop samples were systematically collected, followed by full-diameter core drilling. A range of planar models with varying fracture apertures were designed, and permeability measurements were conducted using the AU-TOSCAN-II multifunctional core scanner with a steady-state gas injection method. The results showed consistency between the improved model and experimental findings regarding the effects of fracture aperture and angle on permeability, confirming the model’s accuracy in reflecting the fractures’ influence on reservoir flow capacity. For intersecting fractures, a comparative analysis of core X-ray computed tomography (X-CT) scanning results and experimental outcomes highlighted discrepancies between actual permeability measurements and theoretical simulations based on tortuosity and aperture variations. Limitations exist, particularly for cross-fractures, where quantifying complexity is challenging, leading to potential discrepancies between simulation and experimental results. Further comparisons between core experiments and logging responses are necessary for model refinement. In response to the challenges associated with evaluating absolute permeability in fractured reservoirs, this study presents a novel theoretical assessment model that considers both single and intersecting fractures. The model’s validity is demonstrated through actual core experiments, confirming the effectiveness of the single-fracture model while highlighting the need for further refinement of the dual-fracture model. The findings provide scientific support for the exploration and development of granite reservoirs in the South China Sea and establish a foundation for permeability predictions in other complex fractured reservoir systems, thereby advancing the field of fracture permeability assessment.

Overall plan optimization of BWB UAV based on comprehensive evaluation

Based on the shape and performance characteristics of the wing-body fusion layout UAV, a multidisciplinary comprehensive analysis model, comprehensive evaluation model and optimization model suitable for the overall scheme of the UAV in the conceptual design stage are established, and the corresponding tools are developed. The geometric, weight, aerodynamic and stealth characteristics of the overall scheme of the wing-body fusion layout UAV are analyzed by combining numerical analysis and engineering methods. The improved TOPSIS method is used to establish a comprehensive evaluation model from the perspective of the loading capacity, economy and adaptability of the UAV. The rationality of the analysis model is verified by taking multiple UAV schemes as examples. Using the parallel subset simulation optimization algorithm, the traditional multi-objective optimization is completed with the maximum cruise lift-to-drag ratio and the minimum forward RCS as the goal, and the scheme optimization based on comprehensive evaluation is completed with the strongest competitiveness as the goal. Based on all the schemes generated by the multi-objective optimization process, the parameter correlation analysis is completed, and the influence of the span, sweep angle and twist angle on the aerodynamic and stealth characteristics is verified. The RCS of the final multi-objective optimization scheme is reduced 41.6%, and the cruise lift-to-drag ratio is improved 3.5% ; the competitiveness score of the comprehensive evaluation optimization scheme is improved 44.0%.

Coronary heart disease evaluation using PCAT radiomics model based on coronary CT angiography and pericoronary adipose tissue.

To explore the clinical application value of radiomics model based on pericoronary adipose tissue (PCAT) in predicting coronary heart disease. A retrospective analysis was performed for inpatients who had undergone coronary computed tomography angiography from January to December 2023, and 164 cases of coronary artery lesions were screened as the lesion group and 190 cases of normal coronary artery samples were selected as the control group. The clinical data and imaging data of all patients were collected, the radiomics features were extracted by relevant software, and the "region of interest" of pericoronary fat was delineated, and the selection operator and multivariate logistic regression were used to screen the radiomic features of pericoronary fat. A coronary heart disease evaluation model was constructed by the best radiomics features. Area under the curve values of the PCAT radiomics scoring model for predicting the receiver operating characteristic curve of coronary heart disease were 0.863 and 0.851 in training and test sets, respectively. After calibration curve analysis, PCAT radiomics scoring model has a high consistency between the predictive evaluation results and the actual results of coronary heart disease events. In addition, in the training set, the PCAT radiomics scoring model has a net benefit on all threshold probabilities. In the test set, the model has a negative net return with only a small number of threshold probabilities. After combining the clinical characteristics model, the evaluation accuracy of the model for coronary heart disease can reach 0.896. PCAT radiomics model based on coronary computed tomography angiography can effectively predict and evaluate coronary heart disease, which is of great value for the clinical diagnosis of coronary artery disease.

Classification and Evaluation of Marginal Land for Potential Cultivation in Northwest China Based on Contiguity and Restrictive Factors

Water, land, and other environmental conditions restrict marginal land (ML) conversion into newly cultivated land. Accurately evaluating ML’s development and utilization potential (DUP) can provide critical support for increasing new cultivated land and ensuring food security. This study focuses on Northwest China, using spatial identification of different types of ML based on remote sensing images, and constructs a county-level DUP evaluation model through contiguous characteristics and restrictive factors to determine new cultivated-land potential, water demand, and liftable grain production. The results show that the DUP of ML in Northwest China is 12.59 million ha, with low-efficiency cultivated land (LCL) and two types of restoration land (TTRL) accounting for 3.29% and 5.95%, and other marginal land (OML) making up 90.76%. The total water demand for ML development and utilization is 69.87 billion cubic meters, which can increase grain production by 62.31 million tons. The coordinated development of water, land, and food promotes an increase in grain production, with water resources being the main restrictive factor. This model effectively evaluates DUP and provides a scientific basis for promoting the rational use of water and land resources. Further research should set up more detailed water resource utilization strategies and scenarios as well as find more development and utilization techniques.

Fuzzy Comprehensive Evaluation based NLOS Identification for UWB Indoor Positioning

Abstract. Ultra-wideband (UWB) positioning technology stands out from many indoor positioning technologies with its advantages of high precision. However, non-line-of-sight (NLOS) propagate leads to heavy range error and reduces position accuracy, this paper proposes a NLOS identification method based on channel impulse response (CIR), which includes three stages. Firstly, CIR based feature selection is carried out, which includes correlation analysis of calculated features. Secondly, fuzzy comprehensive evaluation model is introduced to NLOS identification. Finally, time of arrival (TOA) based location estimation is realized after NLOS ranging error mitigation. Simulation results show that the average identification rate of NLOS can exceed 86%, and the average positioning error can be reduced by about 0.5m.

Evaluation model design of project construction safety level based on bidirectional recurrent neural network (BiRNN) and bidirectional long short-term memory (BiLSTM)

Integrating deep learning methods for multi-element regression analysis poses a challenge in constructing safety evaluations for building construction. To address this challenge, this paper evaluates the integration of construction safety by quantitatively analyzing practitioners’ information and on-site construction conditions. The analytic hierarchy process (AHP) method quantifies construction safety capabilities, considering four key aspects: operators’ primary conditions, organizational personnel’s working conditions, on-site management conditions, and analysis of unsafe behaviors. A comprehensive set of 19 secondary causal factors is constructed. Furthermore, a hybrid model based on bidirectional recurrent neural network (BiRNN) and bidirectional long short-term memory (BiLSTM) is developed for construction safety evaluation, enhancing the model’s generalization ability by introducing the Dropout mechanism. Experimental results demonstrate that the fusion of BiRNN and BiLSTM methods outperforms traditional methods in construction safety evaluation, yielding mean squared error (MSE) and root mean squared error (RMSE) values of 0.48 and 0.69 and mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 0.54 and 3.36%, respectively. The case study affirms that BiRNN-BiLSTM can accurately identify potential safety risks, providing reliable decision support for project management.

Evaluating the Effectiveness of Mobile Interactive Technology in University Physical Education Courses

With the rapid development of mobile interactive technology, a transformative shift in the pedagogical model of university sports courses has been initiated. Traditional teaching methods are simplistic and characterized by their lack of interactivity and personalization. However, the implementation of mobile interactive technology provides students with increased opportunities for participation and access to learning resources. Although existing research focuses on the integration of mobile interactive technology in university sports courses, a significant limitation remains in the evaluation methodologies employed, with a lack of systematic quantitative analysis and a comprehensive evaluation framework. This study proposes an effectiveness evaluation model comprising three primary components: construction of the evaluation model, calculation of indicator weights, and application of the fuzzy comprehensive evaluation method. The objective is to provide a comprehensive understanding of the effectiveness of mobile interactive technology in university sports courses through scientific evaluation methods, offering insights for future educational reforms and technological implementations.

Evaluation of a basic-clinical integrated pharmacology case-based learning program from a student perspective using the CIPP model.

This study aimed to systematically evaluate the effectiveness of case-based learning (CBL) within a basic-clinical integrated educational program using the Context, Input, Process, and Product (CIPP) evaluation model. The CBL program was integrated into the Pharmacology-Clinical Case Practice component of the pharmacology course, a mandatory course for first-year medical students. To evaluate the program, a CIPP model-based questionnaire was developed, assessing needs, goals, resources, educational management, and outcomes. To ensure the reliability and validity of the variables, factor analysis was performed, reducing an initial set of 28 items to 18 final observation variables distributed across four factors. The survey, designed to measure learner satisfaction, was administered to 37 students who participated in the Pharmacology-Clinical Case Practice course during the first semester of 2022. Participants rated their satisfaction with the CBL program based on the CIPP model (on a 5-point scale), giving an average score of 4.17. This suggests that learners who followed the CBL program combining basic and clinical components generally found the program operationally effective with positive outcomes. The teaching model and evaluation model applied in this study can be utilized in various majors when operating CBL classes that link basic and clinical education in medical schools in the future.

Perceptions of medical students at Najran University on the effectiveness of problem-based learning and team-based learning

BackgroundTeam-based learning (TBL) and problem-based learning (PBL) are two effective educational approaches used in medical education for increasing knowledge acquisition, learning motivation, and collaborative and communication skills among students. Few studies have been conducted in Saudi Arabia to evaluate medical students’ perceptions of these approaches.Aim of the studyThe present study aimed to evaluate the perceptions of current or previous preclinical students at the College of Medicine, Najran University (NU) about the effectiveness of PBL and TBL and to assess students’ perceptions regarding the strengths and limitations of both educational approaches and their preferences.MethodsA cross-sectional survey was conducted on 106 preclinical students at NU, Saudi Arabia. The survey questionnaire was validated by a pilot study and designed based on Kirkpatrick’s evaluation model. Descriptive statistics was used to analyze the collected data through IBM SPSS Statistics 2.0.Results73.6% of participants enjoyed PBL and found it motivating compared to TBL and classic lectures. PBL was reported as more effective for knowledge acquisition (50%), teamwork (62.3%), and high academic performance (58%).ConclusionIt is concluded that PBL was more favored by the students regarding the learning outcomes. TBL was reported to need more individual preparation and effort and require lecturer involvement, suggesting the need for structured facilitation in this learning approach and potentially indicating the challenges associated with coordinating team-based activities.

Approximating technology innovation performance with fuzzy approach

Purpose This study aims to construct a framework to evaluate technology innovation performance (TIP) of manufacturing organizations by adopting a fuzzy-based approach. In very short time, the world’s economic order has been reformed by economic globalization, thereby bringing new challenges as well as opportunities for the manufacturing industries. Policy makers encounter different decisions that require the use of various types of data in their decision-making process. These challenges raised the necessity of measuring innovation capability, which is critical issue for decision makers in today’s competitive world. The research results reveal the complexity of the path to technology innovation evaluation, constituting a novel contribution to the literature. Design/methodology/approach It is difficult for decision makers to make appropriate and effective decisions without knowing the innovation capability of companies in a particular sector or a region. As a result, not only on a macro but also on a micro level, an integrated and complete technique of measuring, estimating and even projecting innovation performance is necessary. In light of above mentioned facts, the present study proposes a technology innovation performance evaluation system of manufacturing organizations using fuzzy logic. Findings A model has been developed to be beneficial for any kind of organization where TIP evaluation is important consideration for enhancing manufacturing performance. Fuzzy control is used to determine the overall performance index by combining results of the TIP in selected criteria, which will certainly ensure suitability of the concerned organizations during performance rating calculations. Originality/value This study elaborates a fuzzy model to predict TIP with fuzzy rules. This is the first time a TIP evaluation model is developed using fuzzy approach that will be applicable for any kind of manufacturing industry where TIP evaluation is considered to be significantly important for cooperation’s to sustain competitiveness in the dynamic landscape.

Density gradient structure foams prepared by novel two-step foaming strategy: Performance, simulation and optimization

Functional gradient foam materials play a crucial role in meeting the diverse performance and functionality requirements of modern engineering. Density gradients enable the distribution of mechanical, dielectric, and optical properties within materials, exhibiting a gradient representation. However, creating density gradients within foams poses a significant challenge, especially for semi-crystalline polymers. The paper proposed a novel two-step foaming method for preparing polypropylene (PP) foams with density gradient structure (DGS). Initially, a pre-foaming process was conducted to prepare PP pre-foams with uniform structure (US) at low temperatures, followed by a secondary foaming process on partially saturated PP pre-foams to fabricate PP DGS foams. By adjusting the duration of partial saturation time, PP foams with various DGS can be achieved. Compared with the commonly used one-step foaming method and uniform foaming method in the literature, the DGS foams prepared by the two-step foaming method exhibit not only a significant enhancement in mechanical property but also achieve the lowest thermal conductivity, while maintaining comparable and outstanding sound insulation performance. Finally, a comprehensive evaluation model using COMSOL Multiphysics was developed for the DGS foams, providing insights for optimizing foam performance through process enhancements.

Design of an Energy-Saving System for Smart Home Decoration Based on Situational Awareness

This article analyzes the specific energy-saving requirements for smart home decoration in the current market to determine the overall plan of the entire smart home decoration energy-saving system. Based on situational analysis combined with grounded theory, organize and summarize the original data, and use factor analysis for dimensionality reduction analysis. Combining the multi-level analysis method, experts evaluated and scored the user experience evaluation indicators, obtained the weight assignment of each influencing factor, and constructed a user experience evaluation model for smart home products based on situational theory. After extensive testing, it was found that the ZigBee based smart home network implemented in this article has low energy consumption, overcomes many shortcomings of smart home networks, and has high application value.

Evaluation system of vocational education construction in China based on linked TOPSIS analysis

In response to the task of vocational education reform in the new era, in order to cultivate innovative skilled talents, it is necessary to improve traditional evaluation methods to enhance their efficiency. This study proposes an evaluation model for vocational education construction that combines grey relational analysis and approximate ideal point method (TOPSIS). By selecting indicators and combining subjective analytic hierarchy process and objective entropy weight method for weight allocation, the weights of the indicators are determined using subjective analytic hierarchy process and objective entropy weight method. Through experimental analysis, the results showed that among the 11 analysis indicators, the weight of policy environment indicators was the highest at 0.124, while the weight of employment rate and contract signing rate indicators was the lowest at 0.050. The above results indicate that the TOPSIS comprehensive evaluation system based on grey correlation analysis has better application effects in practical scenarios and is more suitable for the development requirements of vocational education.

Construction and analysis of the EMC evaluation model for vehicular communication systems

With the development of wireless communication technologies, vehicle-mounted wireless communication systems are changing with each passing day, and their EMC has become the key to constraining vehicles to adapt to complex electromagnetic environments. Aiming at the limitations of existing EMC evaluation methods or models for wireless communication systems and the actual needs of EMC evaluation and analysis for vehicular communication systems, a five-level novel evaluation model including working environment, signal spectrum, receiver sensitivity, antenna isolation and communication performance is constructed considering the completeness and accuracy of EMC evaluation for vehicular communication systems. The performance of the constructed model is validated using the vehicular communication system of an armored vehicle as an example. The model can evaluate whether there is interference between the working environment and the signal spectrum of the vehicle-mounted radio station. error in the reduction of the receiver sensitivity between calculation and measurement is 5.8%. The calculated isolation of the vehicle-mounted antenna is in good agreement with the measurements. The modulation mode and coding mode of the vehicle-mounted digital communication system with better performance are simulated and analyzed. When the receiver sensitivity is reduced by 6 dB, the vehicle communication distance is reduced by 50%. The simulation and measurement results show that the proposed model is suitable for the evaluation of electromagnetic compatibility of vehicular communication systems for armored vehicles.

Fuzzy Comprehensive Evaluation-Based Development System for Eco-Agricultural Tourism Resources

This manuscript outlines a systematic evaluation of agricultural eco-tourism resources, focusing on the Taihang Mountain Grand Canyon scenic area. Employing fuzzy mathematics principles, we construct a comprehensive evaluation model to assess various dimensions of agro-ecotourism potential. The framework includes hierarchical levels (A, B1-B3, C1-C8) covering ecological resource value, environment condition, and tourism development. Using the fuzzy comprehensive evaluation method, we determine indicator weights via expert assessments and statistical analyses. Notably, ecological resource value and environment quality emerge as pivotal criteria. The resulting comprehensive score of 82.875 suggests Grade IV suitability for eco-tourism development. Our findings support strategic planning and sustainable management, emphasizing the importance of ecological preservation amidst China's urbanization. Graphical representations and detailed steps provided enhance practicality for sustainable tourism researchers and practitioners.

Cleaning method for abnormal energy big data based on sparse self-coding

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

Sentiment Analysis of Arabic Tweets: Detecting Revilement

Social media systems play an necessary function in shaping public opinion and reflecting societal sentiments. This study focuses on sentiment analysis in Arabic tweets with a particular focus on offensive or offensive content. The aim of this research is to boost a dependable sentiment evaluation model that can accurately classify Arabic tweets as positive or negative, with a particular focus on identifying offensive language. A multinomial Naive Bayes classifier is trained on pre-processed data to perform sentiment classification. The classifier is fine-tuned to differentiate between positive and negative emotions, with a particular focus on identifying offensive or swearing language. The model is evaluated the usage of a complete set of metrics along with precision, precision, recall, and F1score. Experimental consequences point out promising overall performance of the developed sentiment evaluation model. The model achieved an accuracy of 93%, effectively classifying Arabic tweets into effective and bad sentiments. The precision, recall, and F1-score metrics similarly validate the model's capacity to precisely become aware of revilement and offensive language. These outcomes spotlight the conceivable of the proposed strategy in successfully examining Arabic tweets for sentiment and offensive content, contributing to higher grasp on line behaviors and sentiments in the context of revilement.