Traditional Evaluation Research Articles

Abstract 14707: Telehealth Usage for Outpatient Cardiology Evaluations During the COVID-19 Pandemic

Introduction: The use of telehealth for cardiac services rapidly expanded during the pandemic, but little is known about the factors driving use of telehealth versus traditional in-person outpatient evaluation. Research Question: What factors are associated with telehealth use for patients with symptoms of cardiovascular disease referred for outpatient cardiology evaluation during the COVID-19 pandemic? Methods: In a large, integrated healthcare system caring for >4.5M patients, we identified those aged >18 years without prior cardiology diagnoses or visits who were evaluated for symptoms of incident heart failure, coronary disease, or arrhythmias before (Jan 2018-Dec 2019) and during the pandemic (June 2020-May 2022) periods. We categorized patients based on initial cardiology visit type: telehealth (video or telephone) or in-person. We compared unadjusted demographic, socioeconomic (SES), and clinical characteristics between visit types, and examined multivariable associations using Cox regression. Results: Only 146 (2.2%) of 6376 pre-pandemic patients versus 4074 (69%; 1213 telephone, 2861 video) of 5898 pandemic patients had an initial cardiology telehealth visit. In the pandemic period, compared to those with in-person visits, patients with video visits were more likely to be younger (58 vs 64 yrs; p<0.01), female (49% vs 45%; p<0.01), and to be linked to a facility with parking fees (11% vs 7%; p<0.01). Telephone visits had the highest proportion of patients with low SES, low internet usage, and a higher comorbidity burden. In adjusted models, these relationships were generally upheld ( Figure ), and the later year of the pandemic (2022) were strongly associated with increased in-person visits. Conclusions: Many patient and system level factors were associated with telehealth use during the COVID-19 pandemic. Future studies should examine the relationship between clinical outcomes and visit type, as telehealth has become part of routine care.

Application of fuzzy analytic hierarchy process – multi-layer fuzzy inference system in product design evaluation

To enhance profitability of production cycle, any manufacturer needs effective product design and evaluation procedures. This study proposed a novel approach combining fuzzy analytic hierarchy process (FAHP) and multi-layer fuzzy inference system (MFIS). It is based on consumer online comments to improve product design. This method possesses several advantages over traditional design evaluation methods. It can quickly acquire consumer preferences, effectively handle multi-criteria decision problems and integrate uncertain and fuzzy information. The Fuzzy Analytic Hierarchy Process–Multi-layer Fuzzy Inference System (FAHP-MFIS) involves the following steps: screening of factors, hierarchical modeling, quantification of qualitative factors, and conversion of these factors into quantitative values. It is a knowledge-based system that uses logical rules. The quantity and levels of input variables directly correlates with the quantity of logical rules. However, with multi-factor and multi-level inference, the establishment of a rule base becomes impractical due to the overwhelming number of rules. To address this issue, the Taguchi orthogonal table is applied to reduce the number of logical rules. Taking a household oxygen generator for medical devices as an example, the proposed model is applied in real-time. In the first stage, web crawlers are used to collect user reviews of the household oxygen generators on large e-commerce platforms. Latent Dirichlet Allocation (LDA) models are used to screen for principal and sub-factors in the second stage. Then, sub-factors of the FAHP screening are used as inputs, and the principal factors are used as outputs. In the third stage, priority indicators are established based on principal factors such as Appearance, Basic Function, and Advanced Function. Established evaluation models are then used to rank the selected designs. The results show that the higher the priority index value of the product design scheme, the better the scheme, and vice versa. This study holds significant reference value in aiding enterprises to enhance the efficiency of their manufacturing cycle and determining the direction of product design and innovation with improved pace and accuracy. Moreover, it can be applied to other fields such as supply chain management, risk assessment, and investment decisions.

Eğitsel Süreçlerdeki Ortak Platformlarda Etkileşimli Bireysel Değerlendirme ile Sanal Ortamlarda Akademik ve Kültürel Hareketlilik

Abstract The number of common platforms in educational processes is increasing rapidly every day. In addition, the development of the quality and quality of these platforms makes the accessibility to the platforms attractive. In this context, virtual environment-supported technologies have now become indispensable elements of education and training, such as students' and teachers' course materials, in the sense of educational technologies Decoupling. By transferring the prepared courses and exams to this interactive environment, a multi-faceted and timely effective learning environment has been created. Performance evaluation is becoming increasingly important in learning within the framework of today's educational technologies. The learned knowledge should be transferred elsewhere and should be protected and reinforced. Thus, three basic educational elements such as material, interaction and evaluation emerge. Among the main objectives of distance education for effective teaching and training is the integration of different technologies. Dec. In doing so, students should be provided with education without space and time limitations and should not be deprived of interaction with the participant-teacher and other students. As many methods as possible that can meet the student's learning style and needs should be used. In this context, some basic features that should be in distance education have gained importance. Among these features, examples can be given such as the fact that the material to be used is easy to use, academically respectable, and has an interesting and satisfying content for a certain group of students. Dec. In educational methods, knowledge is the most important output of the learning process. The use, storage and most importantly testing of this product, which can also be seen as a surplus value, can take place in multiple interactive environments thanks to information and communication technologies. In addition to the concepts of distance learning and education, the concept of measuring and evaluating what is learned remotely is also gaining validity. Virtual environments where individuals participating in the learning action can test their knowledge after the learning process are becoming widespread. In contrast to traditional measurement and evaluation, measurement and evaluation performed in a virtual environment offers individuals the opportunity to be criticized less, to learn more autonomy, self-confidence and repetition while creating social and environmental pressure. Key Words: Educational Processes, Interactivity, Virtual Environments, Academic and Cultural Mobility

Жизнь и творчество Пань Юэ в контексте его «Оды о губном оргáне»

The research aims to determine the main characteristics of the life and literary works of one of the most prominent Chinese poets of the late 3rd century CE Pan Yue. The paper examines the biography of the author in its various periods and stages, the themes and uniqueness of his works in different traditional genres, as well as the historical fate and significance of his literary legacy. The scientific novelty of the research lies in the fact that it is the first time in Russian Sinology that the life and poetry of Pan Yue become the subject of a specific and comprehensive study: a detailed periodization of his biography is provided, a thematic classification of his works is presented, the related discussions in Chinese tradition are analyzed, the key importance of Pan Yue’s “Rhapsody on the Mouth Organ” for understanding his worldview, creative work and life conflicts is revealed. As a result of the research, the main periods and stages in Pan Yue’s biography have been established; the contradictory nature of traditional evaluations of the poet’s verses has been explained; the main themes of his works with the central theme of mourning for the deceased have been identified; the aesthetic significance of the category of sorrow in Pan Yue’s literary works as a means of restoring harmony and life has been clarified.

A Quality Prediction Method Based on Tri-Training Weighted Ensemble Just-in-Time Learning–Relevance Vector Machine Model

The core quality data, such as interior ballistic performance, are seriously unbalanced in the plasticizing and molding process, which makes it difficult for traditional supervised learning methods to accurately predict this kind of index. A Tri-training weighted ensemble JITL-RVM model based on an integrated confidence evaluation strategy is proposed to solve the above problem. The method is based on Tri-training semi-supervised regression architecture and uses both labeled and unlabeled data for modeling. First of all, the traditional single similarity measure method is difficult to use to evaluate the real similarity between data samples reliably and stably. This method realizes diversity enhancement and data expansion of the data set for modelling through ensemble just-in-time modelling based on three homologous and heterogeneous mixed similarity measures. Secondly, a new integrated confidence evaluation strategy is used to select the unlabeled samples, and the pseudo-labeled data, which can improve the prediction performance of the model, can be selected. To improve the prediction effect of the model, the pseudo-label value of the data is revised continuously. The integrated confidence evaluation strategy can overcome many shortcomings of the traditional confidence evaluation method based on Co-training regression (Coreg). Finally, the final quality prediction value is obtained through weighted integration fusion, which reflects the difference between different models and further improves the prediction accuracy. The experimental results of interior ballistic performance prediction of single-base gun propellant show the effectiveness and superiority of the proposed method, and it can improve the RMSE, R2, and PHR to 0.8074, 0.9644, and 93.3%, respectively.

An Angle Precision Evaluation Method of Rotary Laser Scanning Measurement Systems with a High-Precision Turntable

Rotary laser scanning measurement systems, such as the workshop measurement positioning system (wMPS), play critical roles in manufacturing industries. The wMPS realizes coordinate measurement through the intersection of multiple rotating fanned lasers. The measurement model of multi-laser plane intersection poses challenges in terms of accurately evaluating the system, making it difficult to establish a standardized evaluation method. The traditional evaluation method is based on horizontal and vertical angles derived from scanning angles, which are the direct observation of wMPS. However, the horizontal- and vertical-angle-based methods ignore the assembly errors of fanned laser devices and mechanical shafts. These errors introduce calculation errors and affect the accuracy of angle measurement evaluation. This work proposes a performance evaluation method for the scanning angle independent of the assembly errors above. The transmitter of the wMPS is installed on a high-precision turntable that provides the angle reference. The coordinates of enhanced reference points (ERP) distributed in the calibration space are measured by the laser tracker multilateration method. Then, the spatial relationship between the transmitter and the turntable is reconstructed based on the high-precision turntable and the good rotational repeatability of the transmitter. The simulation was carried out to validate the proposed method. We also studied the effect of fanned laser devices and shaft assembly errors on horizontal and vertical angles. Subsequently, the calibration results were validated by comparing the residuals with those derived from the space-resection method. Furthermore, the method was also validated by comparing the reference and scanning angles. The results show that the maximum angle measurement error was approximately 2.79″, while the average angle measurement error was approximately 1.26″. The uncertainty (k = 1) of the scanning angle was approximately 1.7″. Finally, the coordinate measurement test was carried out to verify the proposed method by laser tracker. The results show that the average re-scanning error was 2.17″.

Innovative development of music education model in universities under stochastic process model analysis

Abstract Based on the analysis of random phenomena, random variables, and random processes, this paper constructs a Markov chain-based model for evaluating the effectiveness of music teaching using Markov chains. Aiming at the core literacy of college music education, a new education model is proposed to cultivate students’ comprehensive music literacy and transform the method of college music education. Traditional evaluation methods and the Markov chain model are used to analyze data examples for the new model of music education. The probability of transferring the number of students whose first score was on the 89.9-80 scale to the 100-90 scale was 0.5115, greater than the probability value of 0.3650 for those still on the 89.9-80 scale. The transfer matrix of the experimental class was shifted to the left as a whole, indicating a high degree of student progress.

Modified response evaluation criteria in solid tumors: A better response evaluation criteria for patients with non-squamous non-small cell lung cancer after bevacizumab treatment.

Cavitation of lesions is common in non-squamous non-small cell lung cancer (non-squamous-NSCLC) patients treated with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFRIs). However, traditional response evaluation criteria in solid tumors (RECIST) do not take cavitation into consideration and may no longer be accurate for potentially reflecting the real clinical efficacy of anti-vessel growth therapy. Here, we aimed to optimize the traditional RECIST version 1.1 by adding cavitation into the evaluation criteria. We performed a post-hoc radiologic review of 517 patients in a phase III clinical trial of bevacizumab biosimilar (SIBP04) combined with chemotherapy for the treatment of non-squamous NSCLC. Tumor responses were assessed by RECIST1.1 and mRECIST criteria (modified RECIST, a novel alternate method where the longest diameter of the cavity was subtracted from the overall longest diameter of that lesion to measure target lesions), respectively, and correlated with clinical outcomes. Cavitations of pulmonary lesions were seen in nine (2%) patients at baseline, and 97 (19%) during treatment. The use of mRECIST resulted in an alteration of the response category. For patients with post-therapy cavitation, the objective response rate was 56% using RECIST1.1 and 67% by mRECIST. In addition, the survival rates between partial response, stable disease, and progressive disease when the mRECIST was applied were significantly different (p<0.05), while RECIST1.1 failed to show survival differences (p=0.218). For patients with post-therapy cavitation, mRECIST exhibited higher predictability of survival than RECIST1.1. Response assessment might be improved by incorporating cavitation into assessment, potentially altering outcomes of key clinical efficacy parameters.

Improving kitchen environment comfort and health: A comprehensive evaluation method based on objective and subjective factors

The kitchen environment exhibits significant differences in both the thermal and humidity environments and air quality relative to typical household settings. Consequently, traditional environmental evaluation indicators, which are applicable to residential areas, are less appropriate for assessing kitchen environments. In existing research, crucial features of the above issue are frequently ignored in favor of either subjective or objective assessments. We developed a thorough evaluation process that combines subjective and objective criteria to address this issue. We utilized a kitchen laboratory to conduct comprehensive tests of key aspects of the kitchen environment, including thermal-humidity conditions, air quality, and acoustic environment under various settings. We then employed the entropy method and the analytic hierarchy process (AHP) to calculate objective and subjective weights, respectively. We determined the general weight of each component using the combination weighting method, leading to the development of the kitchen environment index (KEI) model. Our findings indicated that temperature, sound pressure level, and fine particulate matter concentration significantly impact the kitchen environment, with a total weight of 54.5%. The effectiveness and feasibility of this index were validated through empirical data. With the utilization of our comprehensive approach, we provide a scientific basis for kitchen design and renovation, allowing for improvements in the comfort and health of the kitchen environment.

Automated quantification of small defects in ultrasonic phased array imaging using AWGA-gcForest algorithm

ABSTRACT The imaging quality of ultrasonic phased array technology significantly affects the quantification of small defects inside metals. Moreover, traditional manual evaluation methods require a substantial amount of manpower and time, and they are easily influenced by subjective factors. To mitigate the impact of imaging quality on the automated quantification of defects, this study proposes an improved gcForest algorithm (AWGA-gcForest, Adaptive window genetic algorithm- gcForest). Firstly, the acquired ultrasonic full matrix data from the phased array is processed using full focusing A-scan technique. Additionally, a window averaging weighting method is introduced for ultrasound signal processing to eliminate the interference from incident waves and backwall echoes. Subsequently, a multi-scale adaptive sliding window adjustment strategy based on Mean Absolute Deviation (MAD) is employed to reduce the computational time complexity of the gcForest algorithm. Furthermore, a weighted approach optimised with a genetic algorithm is utilised in the cascade forest to improve the classification accuracy and generalisation capability. Experimental results demonstrate that the proposed method achieves an accuracy of 97.50%, precision of 97.26%, recall rate of 96.63%, and F1 score of 96.92, exhibiting higher detection accuracy compared to other models while also reducing time costs.

Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers

This study introduces an ensemble voting classifier for red wine quality classification using machine learning algorithms. Wine quality assessment, traditionally reliant on subjective expert evaluations, is addressed through data-driven methodologies. The dataset comprises physicochemical attributes and quality ratings of red wines. Results reveal individual models with accuracy ranging from 0.816 to 0.873, while the ensemble approach significantly enhances accuracy. The combination of Random Forest and XGBoost achieves an accuracy of 0.885, demonstrating its potential in red wine quality assessment. In conclusion, this study showcases the potential of machine learning in enhancing the classification of red wine quality, offering a more objective and precise alternative to traditional sensory evaluation. The ensemble voting classifier, especially when combining Random Forest and XGBoost, provides a robust solution for this task, improving the accuracy of wine quality assessments.

An Assessment Model for Sustainable Cities Using Crowdsourced Data Based on General System Theory: A Design Science Methodology Approach

In the quest to understand urban ecosystems, traditional evaluation techniques often fall short due to incompatible data sources and the absence of comprehensive, real-time data. However, with the recent surge in the availability of crowdsourced data, a dynamic view of urban systems has emerged. Recognizing the value of these data, this study illustrates how these data can bridge gaps in understanding urban interactions. Furthermore, the role of urban planners is crucial in harnessing these data effectively, ensuring that derived insights align with the practical needs of urban development. Employing the Design Science Methodology, the research study presents an assessment model grounded in the principles of the city ecosystem, drawing from the General System Theory for Smart Cities. The model is structured across three dimensions and incorporates twelve indicators. By leveraging crowdsourced data, the study offers invaluable insights for urban planners, researchers, and other professionals. This comprehensive approach holds the potential to revolutionize city sustainability assessments, deepening the grasp of intricate urban ecosystems and paving the way for more resilient future cities.

Integration of Social Return on Investment with Health Impact Assessment and evaluation frameworks

Abstract Background To enable sustainable investment in public health to aid recovery from the COVID-19 pandemic and other global adversities, it is necessary to incorporate social, economic and environmental outcomes and value measurement into decision-making processes. Holistic frameworks, such as Social Return on Investment (SROI), look to capture this through understanding stakeholder impact and committing to valuing and monetizing their perceived outcomes. However, opportunities for conducting standalone SROI analyses may be limited due to resources. A solution is to undertake an SROI analysis alongside other analytical frameworks, impact assessments or evaluation methods. Methods In 2023, two innovative case studies in Wales, United Kingdom, integrated SROI into evaluations and assessments. The first is in conjunction with a Health Impact Assessment (HIA) on a sexual health self-testing service in a prison setting, while the second is in combination with an evaluation of a breastfeeding and infant nutrition programme. Results SROI offers a broad framework through which to integrate a holistic health economics lens to different types of evaluation and impact assessment. Traditional evaluations and HIA share common elements with SROI, for example developing a theory of change in evaluations and the screening and scoping stages of a HIA. Learning reflections for future integration were identified, for example ensuring quality assessment approaches align and ensuring design is scoped to enable effective integration from study outset. Conclusions SROI is the predominant tool used to assess the wider value of policies or programmes by identifying and evaluating holistic outcomes which have traditionally been difficult to measure. However, opportunities for standalone SROI studies may be limited for several reasons. As demonstrated, integrating SROI into different methods and approaches offers the opportunity to expand thinking on understanding value in a resource efficient way. Key messages • Social Return on Investment (SROI) can be effectively integrated into different evaluation approaches and Health Impact Assessment. • Integration of approaches has been shown to be resource efficient and beneficial to better understanding value and impact on health and well-being.

A Comprehensive Evaluation on the Performances of China’s Information Technology Characteristic Towns Utilizing the Advantage-Oriented Competitive Evaluation (ACE) Method

The characteristic town (CT, or Te Se Xiao Zhen in Chinese) program is among the most important drivers of China’s new urbanization. However, the program aroused countrywide concerns about its rapid elevation, excessive real estate construction, high investment risk, and severe construction homogenization. Despite the policy’s rapid dissemination, there needs to be a theoretical basis and practical guidance for the local government to evaluate CT candidates to ensure the feasibility and sustainability of this novel urbanization practice. Moreover, simply employing traditional evaluation techniques, such as TOPSIS or analytical hierarchical process, may overwhelmingly stress inherited advantages of existing towns and easily overlook the potential candidates in the policy’s early implementation. Thus, the current study employs a novel advantage-oriented competitive evaluation (ACE) approach, which values the comparative advantages of the evaluated objects, to rate the performance of fifteen information technology characteristic towns (ITCTs) comprehensively. The presented work constructs a three-level performance index system based on public statistics, comprehensively evaluates the ITCTs’ performance, and reveals each CT’s unique advantages. The analysis and discussion disclose the evaluated ITCTs’ development status, highlighting the blooming development against public concerns and the ITCTs’ clustering based on unique comparative advantages. The evaluation results also verify that the ACE is an excellent comprehensive evaluation approach that can reveal an object’s comparative advantages from varying facets and depths. Finally, this work briefly concludes the emerging issues of ITCTs’ construction, the limitations of ACE evaluation, and suggestions for future research.

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

Functional assessment of component positioning in patients with groin pain after total hip arthroplasty as a tool to guide management.

Persisting groin pain post total hip arthroplasty (THA) is a common and complex issue that can be difficult to diagnose and manage. Acetabular component positioning is often implicated. We used a previously well described and validated functional positioning protocol to determine if functional acetabular malpositioning was a factor in groin pain post THA and hence to determine if acetabular revision would be indicated. We compared patient-specific functional acetabular positioning to traditional CT evaluation of cup position and assessment of anterior cup overhang. 39 patients with groin pain post-THA were investigated. Functional acetabular malpositioning was diagnosed in 31% (12/39). Revision THA was performed in those 12 patients, resulting in resolution of functional malpositioning (100%), with an overall accuracy of 5.6° (range 1-12), and resolution of groin pain in 67% (8/12). 33% (4/12) of the revised implants had functional positioning located outside the traditional "40/20 zone". Comparison with CT indicated that 40% (4/10) of implants with anterior overhang were well positioned, however only 50% (6/12) of functionally malpositioned implants had CT evidence of anterior cup prominence. Of the 8/12 revision patients who had resolution of their groin pain, only 1 had cup prominence. This study suggests that the utilisation of a patient specific functional positioning algorithm in the analysis of persistent groin pain following THA can assist in identifying the underlying cause of pain and help to guide treatment. For a functionally malpositioned acetabulum, revision surgery offers a potential resolution of groin pain.

An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services

The rapid urbanization has accelerated the destruction of regional ecosystems, triggering ecological risks and threatening sustainable development. Landscape ecological risk (LER) evaluation is an effective tool to mitigate such negative impacts. However, the existing evaluation systems exhibit certain subjectivity. Therefore, an improved LER evaluation method was proposed, which incorporates ecosystem services (ESs) to characterize landscape vulnerability. The method was validated using the Pearl River Delta urban agglomeration (PRDUA) as the study area. The results showed that the optimal grain size and extent for landscape pattern analysis in the PRDUA were determined to be 150 m and 6km × 6 km, respectively. The comparison results with the traditional LER evaluation method demonstrated the improved method's superior rationality and reliability. The hotspot analysis based on the Getis-Ord Gi* method revealed that the hotspots of LER were mainly concentrated in the densely populated areas of the south-central region of the PRDUA. The coupling coordination degree (CCD) between LERs and ESs showed four different levels of development in both temporal and spatial dimensions, generally dominated by moderately balanced development and lagging ESs, reflecting the unbalanced ecological environment and socio-economic development of the PRDUA. It is recommended that the ecosystems in the PRDUA be managed and protected separately according to the delineated Ecological Protection Area (EPA), Urban Built-up Area (UBA), and Urban Ecological Boundary Area (UEBA). This study can provide an important reference for regional ecosystem conservation and management.

A user review data-driven supplier ranking model using aspect-based sentiment analysis and fuzzy theory

Background:The supplier selection problem is a sophisticated decision-making process that involves evaluating multiple factors. While previous research has primarily focused on objective attributes, such as supplier qualifications, product quality, and price, the subjective opinions of users have often been overlooked. However, with the growing importance of user reviews and sentiment analysis in e-commerce, incorporating users’ opinions on supplier products can provide valuable insights. Purpose:This study aims to address the limitations of existing supplier selection approaches by proposing a comprehensive framework that integrates aspect-level sentiment analysis and a fuzzy multi-attribute decision model. The goal is to enhance the decision-making process by considering both objective attributes and subjective opinions. Methods:To achieve this, we develop a novel convolutional neural network (CNN) model with a gating mechanism to perform aspect-level sentiment analysis. Furthermore, we propose a fuzzy multi-attribute decision model that combines the predefined sentiment aspects with traditional evaluation criteria. The model is applied to a dataset specifically designed for automotive component supplier selection. Results:Experimental results demonstrate the superior performance of our approach compared to existing methods and datasets. A case study demonstrates the combination of aspect-level sentiment analysis and the fuzzy decision model allows for a more comprehensive evaluation of suppliers. Conclusion:By integrating aspect-level sentiment analysis and the fuzzy multi-attribute decision model, our proposed framework offers a novel perspective on supplier selection problems. The results highlight the feasibility and superiority of our approach, providing valuable insights for management in making informed decisions. This research contributes to the fields of supplier selection, sentiment analysis, and decision-making, with potential applications in various industries beyond the automotive sector.

Intelligent Evaluation of Innovative Enterprise Performance - Construction of BPNN Model based on Improved WOA Optimization

With the economic progress, the environment in which enterprises operation is becoming increasingly complex. Intelligent performance evaluation of innovative enterprises is of great significance for their own development. The traditional performance evaluation indicators of enterprises rely too much on their financial indicators, leading producers and operators to pay more attention to the short-term financial performance growth of the enterprise. The long-term development of enterprises is neglected, resulting in weak core competitiveness. Therefore, to better achieve the scientific evaluation of innovative enterprise performance, based on the innovative enterprise performance evaluation index system, an innovative enterprise performance intelligent evaluation model with the whale optimization algorithm optimized backpropagation neural network is constructed. For the shortcomings of the whale optimization algorithm in the operation, the wolf swarm algorithm isintroduced to optimize it. The experimental results show that the evaluation model based on the improved whale optimization backpropagation neural network proposed in the study has very small errors in the evaluation results of different samples, with no more than 3%. This indicates that the performance evaluation index system for innovative enterprises can objectively reflect enterprise performance. This evaluation model can offer a reasonable analysis of enterprise performance, providing reference for intelligent evaluation of innovative enterprise performance.

Energy efficiency evaluation and optimization of active distribution networks with building integrated photovoltaic systems

With the rapid development of building integrated photovoltaic (BIPV) technology, rooftop PV grid-connected systems face challenges in operation. When the consumption rate of BIPV systems is low, power reverse increases distribution network losses and decreases power supply quality. Traditional energy efficiency evaluations for distribution networks do not consider the impact of low BIPV consumption rates on network losses. This paper establishes an energy efficiency evaluation model for distribution networks with BIPV systems based on adversarial interpretive structural modeling and proposes an energy efficiency indicator system applicable to distribution networks with BIPV systems. The method was applied to eight distribution networks with BIPV systems, achieving accurate energy efficiency level rankings. Through single-factor sensitivity analysis of the indicator system, the primary factors affecting distribution networks with BIPV systems energy efficiency were identified. A reactive power optimization model based on multi-objective particle swarm optimization algorithm was established. After optimization, network losses decreased by 153 kW, PV consumption rate increased by 73 %, and node voltage drift was significantly enhanced. Corresponding loss reduction strategies were proposed for other indicators. This paper provides valuable references for planning and renovating distribution networks with BIPV systems.