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Composites are pivotal in Europe's evolving packaging sector, which is undergoing changes due to circular and bioeconomy initiatives, which prioritize eco‐friendly packaging. Despite paper's high recycling rates, dynamic markets challenge recycling and circularity. Innovative paper‐based composites demand flexible recyclability assessments, which impacts product development. Recyclability methods are evolving as they struggle to match the dynamic changes occurring in the packaging sector. This article outlines paper recycling and compares global lab recyclability evaluation methods. [ABSTRACT FROM AUTHOR]

The pulp and paper industry has significant sustainability implications and necessarily requires a careful consideration of direct and lower-tier suppliers for effective sustainable supply chain management. This paper utilized an extensive literature review and semi-structured interviews of experts in the Chinese pulp and paper industry to unearth the factors that highly influence multi-tier supply chain sustainability. A Technological-Organizational-Environmental (TOE) and Human-Organizational-Technological (HOT-fit) based theoretical framework was employed to classify the identified factors. Then, an Analytical Hierarchy Process (AHP) based methodology was applied to determine the relative importance of the factors. A comparison analysis of the relative importance of the factors as determined by the experts in the focal companies, Tier-1 suppliers and Tier-2 suppliers of the Chinese pulp and paper industry is presented. The results show that institutional and technological factors are most critical to actualizing multi-tier supply chain sustainability. Thus, the study outcomes present relevant theoretical and practical implications for the managers and practitioners in the pulp and paper industry on how to facilitate multi-tier supply chain sustainability for increased competitiveness. Furthermore, this study provides guidelines for other industries as well and sets the stage for subsequent theorization and exploration of multi-tier supply chain sustainability for expected performance gains. [ABSTRACT FROM AUTHOR]

With the increasingly strict supervision of the ecological environment and the continuous improvement of pollution control requirements, there are many options for water pollution control technology in the pulp and paper industry. How to choose reasonable and effective control technology has become the focus of attention. In this paper, a water pollution control technology evaluation model based on the Pythagorean language neutrosophic set (PLNS) is proposed for the first time. Firstly, the concept of PLNS is defined and its characteristics are discussed. Then the score function and accuracy function of the Pythagorean language neutral number (PLNN) are defined. To apply PLNNs to multi-attribute decision-making, PLNN power-weighted geometric operator (PLNNPWG) and PLNNPWG geometric Heronian mean operator (PLNNPWG-GHM) are proposed and a multi-attribute decision-making method is proposed. Finally, this method is used to evaluate the water pollution control technology of the pulp and paper industry. Through the experimental results, it can be seen that if the comprehensive weights are used, elemental chlorine-free (ECF) clean bleaching technology is the best choice, when using single weights to calculate, in technical characteristics, ECF clean bleaching performed best. In economic cost, chlorination, enhanced alkali treatment, hypochlorite (CEH) three-stage bleaching performed best. In operation management and environmental impact, ECF clean bleaching and total chlorine-free (TCF) clean bleaching had almost the same score. This model can provide a reliable basis for enterprises to choose the appropriate water pollution control technology and also provide strong support for the management department to recommend the best feasible technology. [Display omitted] • Propose the Pythagorean language neutrosophic set (PLNS). • Define the score function and accuracy function of Pythagorean language neutral number (PLNN). • Propose the PLNN power-weighted geometric (PLNNPWG) and PLNNPWG geometric Heronian mean (PLNNPWG-GHM) operators. • Propose a decision-making method to evaluate the water pollution control technology of pulp and paper industry. • Provide strong support for the management department to recommend the best feasible technology. [ABSTRACT FROM AUTHOR]

The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer. [ABSTRACT FROM AUTHOR]

In order to obtain stable and reliable configuration transformation ability, reliability optimization design is regarded as an effective way to reduce the probability of kinematic function failure for the constrained metamorphic mechanism. Based on the structural composition principle of multi-configuration source metamorphic mechanism that can operate in an under-actuated state, the modularized calculation methods are established for the force analysis of augmented Assur groups including metamorphic kinematic joints. According to the equivalent resistance gradient model of metamorphic mechanisms, with considering the uncertainties in the link dimensions, masses, and compliance parameters et al., a probabilistic evaluation method for describing the configuration transformation ability of the constrained metamorphic mechanism is established. Based on reliability evaluation and reliability sensitivity analysis, a reliability optimization design method for improving the configuration transformation ability is proposed, and then the optimization design is carried out for tolerances of random variables focusing on those structural parameters with higher reliability sensitivity, so that the optimized results can satisfy the requirements of both reliability and economic simultaneously. Finally, the feasibility and effectiveness of the proposed method is verified by the illustration of a paper folding metamorphic mechanism. The research provides the foundation of reliability design of metamorphic mechanisms to obtain the high-probability repeated execution ability of configuration transformation, it also has theoretical and practical significance to promote the engineering application of metamorphic mechanisms. [ABSTRACT FROM AUTHOR]

The insulation performance of oil-immersed paper bushings is prone to deteriorate, primarily due to moisture intrusion and thermal aging. The frequency domain spectroscopy (FDS) method is commonly employed to assess the insulation condition of the bushing. However, identifying and extracting relaxation polarization information from the low-frequency region of the FDS curve can be challenging, and there is little research about the condition evaluation under the combined effects of aging states and moisture content. To address this issue, this article uses the Taylor formula mathematical model to extract characteristic parameters from the dielectric modulus curve of OIP bushings and uses the KNN algorithm to achieve the evaluation of aging and moisture status. Then, the effectiveness and accuracy of the proposed method are validated on three field OIP bushings. The results demonstrate that the evaluation accuracy of the proposed method exceeds 83%, which has significant advantages compared to other classification algorithms. The innovation of this article lies in extracting new feature parameters and combining them with intelligent classification algorithms to evaluate the moisture and aging state of the bushing. [ABSTRACT FROM AUTHOR]

The structure of the fibre network in tissue paper can be complex and difficult to analyze, due to the presence of superimposed structures such as creping and patterns that occur, for example, in through-air-dried (TAD) tissue. Properties like high absorbency, a pleasant handfeel, and strength-related characteristics are closely related to the fibre network structure. Therefore, in addition to standard tissue testing methods, techniques that provide insights into the intrinsic properties of the tissue fibre network are essential for a deeper understanding and potential for further optimization. In this study, we utilized 2D cross-sectional images and 3D X-ray microtomography (μ -CT) to evaluate and quantify the intrinsic properties of highly structured TAD tissue. We compared the results obtained from these two methods, focusing on intrinsic thickness, porosity, and the fibre volume to fibre surface area (Fv/Fs) ratio. The open structure of the fibre network, fabric patterns, creping, and protruding fibres make it challenging to define bulk boundaries. Therefore, we examined the effect of different bulk expansion diameters on intrinsic properties. This procedure allows to quantify the effects of under- and overestimation of bulk boundaries, and to determine which regions within the fibre network are affected by bulk expansion. In terms of intrinsic thickness, both 2D and 3D evaluations show similar trends, which facilitates direct comparison of 2D and 3D data. Porosity, on the other hand, does not show any correlation between 2D and 3D-based data. Together with the Fv/Fs parameter, this leads to the conclusion that the depiction of 2D data does not represent the whole fibrous material but predominantly fibres perpendicular or close to perpendicular to the cut plane, whereas 3D data represents all fibres, fibre bonds and network connectivity. This work aims at presenting modern approaches and novel procedures to quantify intrinsic properties of open fibre structures such as tissue, but could also be applied to fibrous networks in general. The introduced methods could provide the basis for future research on the interrelations between intrinsic properties and key tissue properties such as absorbency and handfeel. [ABSTRACT FROM AUTHOR]

The transparency, reproducibility, and ethics movement has helped promote efforts to improve the rigour and validity of social science research. However, replication research, where the original data is used to check the validity and robustness of the estimations and recommendations in the original paper, has not been widely used as a tool in this field. This article outlines a checklist and standardised process that can guide replication researchers in creating and justifying their replication plans. To create the checklist, we reviewed 31 resources that were identified through unstructured keyword searches. We also reviewed and coded replication studies from 3ie's Replication Program which includes more than 20 3ie-funded replication papers and guidance on our replication process. There were five categories of potential validation tests and robustness checks that researchers could include. The checklist attributes range from testing specific assumptions for select impact evaluation methods to data transformations or dealing with heterogeneity. We hope that this checklist helps stimulate demand for replication research and promotes the use of replication research as a tool within transparency, reproducibility and ethics. [ABSTRACT FROM AUTHOR]

The fossils of Hamipterus tianshanensis (Wang et al. in Curr Biol 24:1323–1330, 2014) and their eggs have important scientific significance because they can provide unique information about the reproduction, development, and evolution of pterosaurs. The fossils and the rock surrounding them have, however, been weathered, which including powdering and flaking, since they were relocated from Xinjiang to Beijing. The high content of soluble salts is a significant factor in fossil deterioration because the dissolution–recrystallization process can generate tremendous pressure and lead to decreased mechanical strength. This study evaluated the electrokinetic desalination performance for the fossils, and two types of poultices employed including paper pulp from Bioline® and CKS121 (cellulose: kaolin: sand = 1:2:1, w/w). Mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), ion chromatography (IC), and other methods were applied to evaluate the desalination effect. The surface salt content reduction by applied direct current (DC) was about 70%, and the inner salt content reduction was about 80%. The experimental results suggest that the electrokinetic method is a promising way to desalinate fossils. Nonetheless, cracks appeared in the surrounding rock crack after electrokinetic desalination, which can be explained by the montmorillonite swelling-induced stresses. Pre-consolidation, especially for electro-chemical method may solve the cracking problem for the clay-rich sandstone desalination. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present an exhaustive analysis on research paper recommender systems which have become very popular and gained a lot of research attention. Though the major focus is on developing new recommendation algorithms, other research dimensions are left untouched. Renown recommendation classes include content-based approaches, collaborative filtering, link-based algorithms, co-occurrence based approaches, global relevance and hybrid methods. These approaches mainly differ in background knowledge and modes of user behavior analysis. For instance, content-based filtering uses paper descriptions which are mostly word-based features. Collaborative filtering makes predictions based on peers' interests. Link-based algorithms utilize academic associations that exist between different entities in academia. Co-occurrence based techniques incorporate event occurrences to locate related papers. Global relevance adopts a 'one-for-all' policy for recommending popular articles. Hybrid methods combine the above approaches to design efficient algorithms. We have reviewed articles implementing several versions of these classes, however minor customizations make it difficult to categorize the new methods to one of the base classes. We have defined the concept of 'background knowledge and operating principle' for proper classification and to make a clear distinction among the recommendation approaches. We have used a combination of systematic literature review, critical review, and conceptual review to conduct this survey which presents current advancements in the field and discusses popular recommendation approaches. This paper reveals the factors affecting users' behavior and introduces a taxonomy of knowledge acquisition sources. Moreover, various evaluation methods and important performance criteria are explored. Finally, this paper examines the research trends and reports major loopholes in current research to foster the development of efficacious recommender systems. [ABSTRACT FROM AUTHOR]

With the burgeoning expansion of digital technologies, electronic displays are becoming increasingly prevalent in our daily lives. These displays are not limited to stationary ones like traditional TVs but have also found their way into mobile devices such as smartphones and wristwatches. To facilitate this significant shift, it is evident that substantial engineering efforts are necessary. However, equally crucial is the establishment of proper methods for measurement and evaluation. Without the development of such evaluation methods, technological progress would be hindered. [ABSTRACT FROM AUTHOR]

This letter points out a conceptual error made by the authors of a published paper, which presents a review and evaluation of computational methods in lncRNA identification. The error was made in the execution of the BASiNET method when considering an example file (toy model) made available by the authors with the aim of showing how a classification model could be stored in a file for later use. In this letter, this error is contextualized, the correct use of the BASiNET method is pointed out and the results of its correct execution to one of the datasets used in the review article are presented. The results clearly show the misuse of the method and present its correct use so that it can be fairly compared with other methods in the literature and prevent its misuse from being replicated by new studies. [ABSTRACT FROM AUTHOR]

Highly fibrillated cellulose, including cellulose micro or nanofibrils (CMFs or CNFs), has been extensively investigated. However, its morphological properties defined as the degree of fibrillation vary over time according to the fiber sources and preparation methods. In this study, a facile method for evaluating the degree of fibrillation was developed using a conventional Canadian standard freeness (CSF) tester and the obtained values were called drainage volumes (DVs). The standard screen used in the CSF test is used to retain fibers and fibril, but the screens in the DV test to fractionate the fibrils by their size. Two DVs of the highly fibrillated cellulose were obtained using two special mesh size screens in the DV test. The differences between two DVs were found to have a linear relationship with the degree of fibrillation and the average fibril width of the highly fibrillated cellulose. They also exhibited a linear relationship with the crystallinity, degree of polymerization and the mechanical properties of the fibril-based sheets. Two commercial CNFs produced by paper mills with different wood sources were evaluated using the developed method. Their DV differences enabled the estimation of their degree of fibrillation, with which the average fibril widths and physical and mechanical properties were predicted and they were found to be well matched. [ABSTRACT FROM AUTHOR]

The test methods for flame propagation of long elements of the electrical wiring system, in particular, cables, cable conduits and ducts, are analyzed, and differences in them are found in the test conditions and criteria for evaluating the resistance to flame propagation. Using a substrate of a wooden board covered with a layer of tissue paper with an areal density of (21±9) g/m2, adopted for testing other elements of the electrical wiring system, a cable was identified that is not resistant to flame propagation. It is proposed to use this substrate for testing the flame propagation of cables instead of a substrate made of a double layer of filter paper with a surface density of (80±15) g/m². In one of three experiments, a cable that was not resistant to flame propagation was found based on the criterion of the presence of ignition of the substrate located under it. To reduce the risk of making an incorrect decision on compliance, it is proposed that the assessment of long elements of the wiring system be carried out according to the rules established for cable ducts, trays and ladders in EN 50085-1 and IEC 61537. For the AVVG cable with an outer diameter of 10 mm to 60 mm, when it touches the blue flame cone of 1 kW, the correlation coefficient of the dependence of the length of the charred part on the diameter was 0.969. For a distance of 100 mm between the sample and the burner along its axis, a correlation coefficient of 0.985 was obtained. It is proposed to test cables under the second condition recommended in IEC 60695-11-2. For two conduits, flame propagation was revealed when exposed to a 1 kW flame for 120 s and 240 s. However, for these pipelines, flame propagation did not occur under standard conditions of exposure to such a flame for 20 s and 25 s. To identify long elements of the wiring system that are not resistant to flame propagation, it is proposed to test them at a duration of exposure to a flame of 1 kW, established for cables in IEC 60332-1-2 [ABSTRACT FROM AUTHOR]

Display front‐of‐screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defective samples, has been a long‐standing problem that hinders the successful application of deep learning algorithms. Synthetic defect data generation can help address this issue. This paper reviews the state‐of‐the‐art synthetic data generation methods and the evaluation metrics that can potentially be applied to display FOS quality inspection tasks. [ABSTRACT FROM AUTHOR]

Likert scales are often used in visualization evaluations to produce quantitative estimates of subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to collect, analyze, and visualize data collected with Likert scales are inconsistent among evaluations in visualization papers. In this paper, we examine the use of Likert scales as a tool for measuring subjective response in a systematic review of 134 visualization evaluations published between 2009 and 2019. We find that papers with both objective and subjective measures do not hold the same reporting and analysis standards for both aspects of their evaluation, producing less rigorous work for the subjective qualities measured by Likert scales. Additionally, we demonstrate that many papers are inconsistent in their interpretations of Likert data as discrete or continuous and may even sacrifice statistical power by applying nonparametric tests unnecessarily. Finally, we identify instances where key details about Likert item construction with the potential to bias participant responses are omitted from evaluation methodology reporting, inhibiting the feasibility and reliability of future replication studies. We summarize recommendations from other fields for best practices with Likert data in visualization evaluations, based on the results of our survey. A full copy of this paper and all supplementary material are available at https://osf.io/exbz8/. [ABSTRACT FROM AUTHOR]

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain. [ABSTRACT FROM AUTHOR]

The common perception of people about recycling involves reusage of aluminum cans, glass bottles, and newspapers, whereas recycling of water resources is a most important aspect nowadays. Water recycling is known to reuse cleaned wastewater for beneficial applications including agricultural and landscape irrigation, industrial activities, and replenishing a groundwater using the latest technologies. The current methods have some flaws in the evaluations of water resources' recycling systems such as large mean square error, time complexity, and low-evaluation efficiency; therefore, this paper proposes an online evaluation method for the recycling process of water resources in urban landscaping. The health model of water resources recycling in urban landscaping has been analyzed using fuzzy-based approach. Second, the evaluation index system of water resources' recycling is also analyzed using the ecological water-level analysis results, water resource quality, water resource abundance, and water resource utilization rate. Then, the extension of analytic hierarchy process (AHP) is utilized to calculate the weight of water recycling evaluation index. Then a fuzzy-based comprehensive evaluation method is used to find the online evaluation model of the water recycling. Eventually, the evaluation of the effect of water recycling in urban landscaping is performed and analysis is made for decision-making. The results prove that the proposed AHP and fuzzy method has a low mean square error and high accuracy. [ABSTRACT FROM AUTHOR]

Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals. [ABSTRACT FROM AUTHOR]

Efficiency and accuracy are two major criteria for evaluating a test method. The thermal stability evaluation of PVC materials is now mainly based on the color change of PVC samples and Congo red test paper with manual observation as the main method, which is inefficient and imprecise. In this work, a machine vision-based evaluation system was developed to address the issues of low efficiency and poor accuracy in evaluating PVC thermal stability. During the degradation process, the color change recognition characteristics of PVC film samples and Congo red test paper were determined by analyzing the RGB SUM value and the R value, respectively. In comparison to manual observation, the machine vision-based evaluation system is more objective, efficient and accurate, and it is particularly suitable for evaluating PVC samples with similar thermal stability. Moreover, this study also resolves the issue of color mutation during the window period of the thermal stability test, and obtains the thermal degradation evolution trend through the color value change curve. This method holds great importance and application potential in guiding the processing or reprocessing of PVC materials. [ABSTRACT FROM AUTHOR]

Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles. [ABSTRACT FROM AUTHOR]

• Recommend citations from the perspective of cited paper instead of citing paper. • Design four forms of citation content for different citation motivations. • Design 132 methods for generating semantic representation of cited paper. • Find the most suitable form and method through quantitative comparisons. Citation recommendation can help researchers quickly find supplementary or alternative references in massive academic resources. Current research on citation recommendation mainly focuses on the citing papers, resulting in the enormous cited papers are ignored, including the relations among cited papers and their citation context cited in citing papers. Moreover, cited paper's content is often denoted with its original title and abstract, which is hard to acquire and rarely considers different citation motivations. Furthermore, the most appropriate method for semantic representation of cited papers' relations and content is uncertain. Therefore, this paper studies citation recommendation from the perspective of semantic representation of cited papers' relations and content. Firstly, four forms of citation context are designed and extracted as cited papers' content considering citation motivations, as well as co-citation relationships are extracted as cited papers' relations. Secondly, 132 methods are designed for generating semantic vector of cited paper, including four network embedding methods, 16 methods by combining four text representation algorithms with four forms of citation content, and 112 fusion methods. Finally, similarity among cited papers is calculated for citation recommendation and a quantitative evaluation method based on link prediction is designed, to find the most appropriate form of citation content and the optimal method. The result shows that doc2vecC (Document to Vector through Corruption) with the form of CS&SS (Current Sentences and Surrounding Sentences) performs best, in which the AUC (Area Under Curve) and MAP (Macro Average Precision) reach 0.877 and 0.889 and have increased by 0.462 and 0.370 compared with the worst-performing method. This performance is slightly improved by parameters adjustment, and a case study is performed whose results have further proved the effectiveness of this method. In addition, among four forms of cited papers' content, CS&SS performs best in almost all methods. Furthermore, the fusion methods not always perform better than the single methods, where doc2vecC (CS&SS) performs better than the best fusion method GCN (Graph Convolutional Network). These results not only prove the effectiveness of citation recommendation from the perspective of cited paper, but also provide helpful and useful suggestions for method selection and citation content selection. The data and conclusions can be extended to other text mining-related tasks. Simultaneously, it is a preliminary research which needs to be further studied in other domains using emerging semantic representation methods. [ABSTRACT FROM AUTHOR]

The integrity of peer review is essential for modern science. Numerous studies have therefore focused on identifying, quantifying, and mitigating biases in peer review. One of these better-known biases is prestige bias, where the recognition of a famous author or affiliation leads reviewers to subconsciously treat their submissions preferentially. A common mitigation approach for prestige bias is double-blind reviewing, where the identify of authors is hidden from reviewers. However, studies on the effectivness of this mitigation are mixed and are rarely directly comparable to each other, leading to difficulty in generalization of their results. In this paper, we explore the design space for such studies in an attempt to reach common ground. Using an observational approach with a large dataset of peer-reviewed papers in computer systems, we systematically evaluate the effects of different prestige metrics, aggregation methods, control variables, and outlier treatments. We show that depending on these choices, the data can lead to contradictory conclusions with high statistical significance. For example, authors with higher h-index often preferred to publish in competitive conferences which are also typically double-blind, whereas authors with higher paper counts often preferred the single-blind conferences. The main practical implication of our analyses is that a narrow evaluation may lead to unreliable results. A thorough evaluation of prestige bias requires a careful inventory of assumptions, metrics, and methodology, often requiring a more detailed sensitivity analysis than is normally undertaken. Importantly, two of the most commonly used metrics for prestige evaluation, past publication count and h-index, are not independent from the choice of publishing venue, which must be accounted for when comparing authors prestige across conferences. [ABSTRACT FROM AUTHOR]

The advancement of Augmented Reality (AR) technology has been remarkable, enabling the augmentation of user perception with timely information. This progress holds great promise in the field of interaction design. However, the mere advancement of technology is not enough to ensure widespread adoption. The user dimension has been somewhat overlooked in AR research due to a lack of attention to user motivations, needs, usability, and perceived value. The critical aspects of AR technology tend to be overshadowed by the technology itself. To ensure appropriate future assessments, it is necessary to thoroughly examine and categorize all the methods used for AR technology validation. By identifying and classifying these evaluation methods, researchers and practitioners will be better equipped to develop and validate new AR techniques and applications. Therefore, comprehensive and systematic evaluations are critical to the advancement and sustainability of AR technology. This paper presents a theoretical framework derived from a cluster analysis of the most efficient evaluation methods for AR extracted from 399 papers. Evaluation methods were clustered according to the application domains and the human–computer interaction aspects to be investigated. This framework should facilitate rapid development cycles prioritizing user requirements, ultimately leading to groundbreaking interaction methods accessible to a broader audience beyond research and development centers. [ABSTRACT FROM AUTHOR]

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. The evaluation criteria are based on the values of indexes for pumped storage plants that have already been put into operation. Using this method, the operational effect of pumped storage plants with different installed capacities, regulation durations, and conversion efficiencies are comprehensively evaluated and analyzed. The calculation results show that the operation effect of a pumped storage plant with high regulation performance and high comprehensive conversion efficiency is better, indicating that the established index system and evaluation method can comprehensively and truly reflect the positive benefits brought by a pumped storage plant to a new power system. This study can provide a practical reference for the early planning and decision making of pumped storage in a new power system. [ABSTRACT FROM AUTHOR]

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering. [ABSTRACT FROM AUTHOR]

A high proportion of distributed generators (DGs) connected to the distribution network causes a significant change in the normal and fault currents of the system as well as in the linearization of the characteristics. It is difficult to adapt to conventional protection. This paper theoretically analyzes the possible impact of fault current characteristics on traditional protection based on single-ended informativeness after connecting to DGs. From the perspective of protection action, the evaluation index system of DG protection is established by considering the maximum short-circuit current output from DG. Combined with the relay protection requirements, the calculation method of evaluation indexes is given concerning the protection characteristics and expert experience. An analytic hierarchy process (AHP) and a CRITIC combination assignment method based on the principle of minimum information identification are proposed. The scores of different types of protection before and after DG access are calculated using the proposed methodology employing a typical distribution network example. The proposed method can quantitatively obtain the distribution network protection adaptability boundary. In the actual calculation example selected in this paper, a DG can reasonably improve the adaptability of the three-stage current protection when it increases the current amplitude at a penetration rate of 50%; the DG needs to adjust the three-stage current protection rectification value when it decreases the current amplitude at a penetration rate of 20%; and adaptive overcurrent protection and inverse time limit current protection need to be adjusted when the penetration rate of DG is 50%. Compared with the traditional protection evaluation method, the method adopted in this paper can intuitively derive the weak link between protection handling faults after DG access as well as the appropriate capacity of DG to improve protection performance. It can provide a powerful reference for the optimization of protection schemes after the high percentage of DG access. [ABSTRACT FROM AUTHOR]

• Multiple evaluation methodologies used in retinal vessel segmentation identified. • Published scores in 100 papers categorized by the methodology they are based on. • The scores are numerically adjusted to eliminate the biases for comparability. • A new baseline ranking of segmentation techniques provided. • The comparison of incomparable scores is an ongoing phenomenon in the literature. [Display omitted] In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise. [ABSTRACT FROM AUTHOR]

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multifaceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the past 7 years at major AI and ML conferences that introduce an XAI method. We find that one in three papers evaluate exclusively with anecdotal evidence, and one in five papers evaluate with users. This survey also contributes to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. Our systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark, and compare new and existing XAImethods. The Co-12 categorization scheme and our identified evaluation methods open up opportunities to include quantitative metrics as optimization criteria during model training to optimize for accuracy and interpretability simultaneously. [ABSTRACT FROM AUTHOR]

Printing quality control has been one of the challenges in the printing industry. Therefore, it is indispensable to find evaluation methods with high correlations with the human visual system to assess the quality of the printed images. Standard ISO/IEC 24790 is one of the recommended standards to achieve this goal. An evaluation procedure for graininess, a common defect in printing, has been presented in this standard; however, this procedure is suggested only for monochrome printed images. Hence, in this research, the graininess of coloured samples is evaluated based on the proposed method in this standard with considering the effect of the paper. For this purpose, fictitious graininess defects are designed and applied on coloured samples and are printed on different papers. A visual assessment is performed to examine the effect of colour and paper differences on graininess. Six different methods, including the method suggested in the standard and the proposed modified procedures, are used to evaluate graininess, and their results are compared to subjective results. Moreover, three other wavelets are studied besides the suggested one in the standard. To eliminate the effect of paper texture, a Gaussian filter is utilised. The results show that applying the Gaussian filter to images increases the correlation between computational procedure results and visual assessment results. Also, the results indicate that utilising haar wavelet slightly increases the correlation. The results demonstrate that besides assessing graininess in greyscale mode, evaluating graininess in RGB (red, green, blue) colour space results in a high correlation with the visual assessment results. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of public health emergencies has affected people's daily lives. In response, this paper evaluates the risk of logistics terminal distribution and puts forward the corresponding countermeasures. Firstly, this paper summarizes relevant literature and analyzes the characteristics of public health emergencies and logistics terminal distribution. This paper constructs the risk index system of logistics terminal distribution under public health emergencies for the first time. Secondly, this paper conducts a questionnaire survey on a domestic e-commerce enterprise. In this paper, the intuitionistic fuzzy entropy weight method is applied to the obtained data to determine the weight of risk index. Finally, this paper adopts the grey fuzzy comprehensive evaluation method to evaluate the risk index of logistics terminal distribution under public health emergencies. This paper takes the impact of public health emergencies into account for the risk of logistics terminal distribution for the first time. Some suggestions are provided for preventing the logistics terminal distribution from stagnation and paralysis when public health emergencies occur. This paper supplements the existing research in this field. [ABSTRACT FROM AUTHOR]

This paper proposes an evaluation method for an optimal scene capture for a 3D display. The method analyzes multi-view data to determine capture guidelines. An user study was conducted to verify the proposed method and to discover the distortion limits of the optimal capturing camera positions. The discovered limits are 0.3 ∘ in rotations and 4px in the translation of the area of interest. Different variants of distortions were compared and their significance in visual degradation was evaluated. Rotational distortions were the most tolerated by users, followed by translational and combined. The solid-color background and suppressed head motion increase the user's tolerance to the artifacts. The paper also proposes a 2D-reprojection-based correction method for unsuitably captured scenes. In some cases, this reprojection increased the measured limits by up to 60 × compared to the uncorrected distortions and proved to be effective. This study supplements existing studies with a large set of previously unexplored measurements and provides results relevant to current state-of-the-art 3D displays. It reveals important information for possible future 3D display rendering or capture methods. The images captured and corrected according to the proposed method can be displayed directly on a 3D display by Looking Glass Factory, creating a 3D effect without additional VR or AR equipment. [ABSTRACT FROM AUTHOR]

Sign language (SL) avatar systems aid communication between the hearing and deaf communities. Despite technological progress, there is a lack of a standardized avatar development framework. This paper offers a systematic review of SL avatar systems spanning from 1982 to 2022. Using PRISMA guidelines, we shortlisted 47 papers from an initial 1765, focusing on sign synthesis techniques, corpora, design strategies, and facial expression methods. We also discuss both objective and subjective evaluation methodologies. Our findings highlight key trends and suggest new research avenues for improving SL avatars. [ABSTRACT FROM AUTHOR]

As a distributed database, the system security of the blockchain is of great significance to prevent tampering, protect privacy, prevent double spending, and improve credibility. Due to the decentralized and trustless nature of blockchain, the security defense of the blockchain system has become one of the most important measures. This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense, and we first introduce the overview, classification, and threat assessment process of blockchain security threats. Then, we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize the blockchain security evaluation indicators and evaluation methods. Finally, we discuss the challenges of blockchain security and future research directions, such as parallel detection and federated learning. This paper aims to stimulate further research and discussion on blockchain security, providing more reliable security guarantees for the use and development of blockchain technology to face changing threats and challenges through continuous updating and improvement of defense technologies. [ABSTRACT FROM AUTHOR]

This research paper highlights and addresses the lack of a systematic review of the methods used to evaluate Learning Analytics (LA) and Learning Analytics Dashboards (LAD) of Adaptive Learning Platforms (ALPs) in the current literature. Addressing this gap, the authors built upon the work of Tretow-Fish and Khalid (2022) and analyzed 32 papers, which were grouped into six categories (C1-6) based on their themes. The categories include C1) the evaluation of LA and LAD design and framework, C2) the evaluation of user performance with LA and LAD, C3) the evaluation of adaptivity, C4) the evaluation of ALPs through perceived value, C5) the evaluation of Multimodal methods, and C6) the evaluation of the pedagogical implementation of ALP's LA and LAD. The results include a tabular summary of the papers including the categories, evaluation unit(s), methods, variables and purpose. While there are numerous studies in categories C1-4 that focus on the design, development, and impact assessment of ALP's LA and LAD, there are only a few studies in categories C5 and C6. For the category of C5), very few studies applied any evaluation methods assessing the multimodal features of LA and LADs on ALPs. Especially for C6), evaluating the pedagogical implementation of ALP's LA and LAD, the three dimensions of signature pedagogy are used to assess the level of pedagogy evaluation. Findings showed that no studies focus on evaluating the deep or implicit structure of ALP's LA. All studies examine the structural surface dimension of learning activities and interactions between students, teachers, and ALP's LA and LAD, as examined in categories C2-C5. No studies were exclusively categorized as a C6 category, indicating that all studies evaluate ALP's LA and LAD on the surface structure dimension of signature pedagogy. This review highlights the lack of pedagogical methodology and theory in ALP's LA and LAD, which are recommended to be emphasized in future research and ALP development and implementation. [ABSTRACT FROM AUTHOR]

In order to improve the comprehensive ability evaluation effect of students majoring in preschool education, this paper combines computer technology to evaluate the comprehensive ability of students majoring in preschool education. Through linearization, this paper deduces the state space model of data load of students' comprehensive ability and the transfer function model of student's comprehensive ability data load. Moreover, this paper establishes an analytical expression between the model coefficients of the transfer function of student's comprehensive ability data load and the transfer parameters of student's comprehensive ability data load. In addition, this paper analyzes and selects the identification method of the parameters of students' comprehensive ability data load model. Since the parameters of the student comprehensive ability data load model have physical meanings, the parameter identification of the student comprehensive ability data load model is a gray box identification, and constraints are required when identifying the parameters of the student comprehensive ability data load model. The experimental study has verified that the comprehensive ability evaluation model of students majoring in preschool education based on computer technology can play an important role in the comprehensive ability evaluation of students majoring in preschool education. This article adopts the comprehensive load model in the Comprehensive Stability Calculation Program (PSASP) to eliminate the shortcomings of factor analysis in traditional teaching comprehensive evaluation, and has a certain reference value for the promotion of intelligent teaching in the future. [ABSTRACT FROM AUTHOR]

Background: As of 2020, 20% of people residing in the United States of America (U.S.) lived in rural communities. Despite rural residents tending to be older, poorer, and having greater disease burden than their urban counterparts, the number of rural primary care providers continues to decline. Nearly 66% of U.S. Primary Care Health Professional Shortage Areas are designated as rural. Pharmacists can help address this shortage of rural primary care providers, often serving as providers of first-contact care; however, only 12% of U.S. pharmacists practice in rural communities. To help address this gap, in 2022 an elective Rural Pharmacy course was created at the University of Minnesota College of Pharmacy by a faculty member who has rural practice experience. Methods: The course combines formal lectures, guest presentations by rural pharmacists and student interviews with additional rural pharmacists. For the 42 students enrolled in the course in 2022 and 2023, non-parametric statistics were used to compare the percentage of students who were raised in rural communities or who otherwise had extensive exposure to rural, and compare student interest ratings (1 to 7) about practicing/living rural at the beginning and end of the course. Students also wrote end-of-course reflection papers, commenting on the course and their interviews with rural pharmacists. Results: Across both years, 45% of the enrolled students had previous experience in rural communities. The net change in Rural Interest scores among students completing both questionnaires was + 5 in 2022 and + 2 in 2023, both non-significant differences. The largest shifts in student interest were from "Not Sure" at the start of the course to "Interested" or "Not Interested" at the end of the course, and from "Interested" to "Very Interested." In their reflection papers nearly 60% of students reported being most impressed by their interviews with rural pharmacists. Conclusions: A course addressing the benefits and challenges of practicing pharmacy in rural communities was well-received by pharmacy students. Even students who have little interest in living in a rural community can benefit from being introduced to rural culture, enabling them to provide more culturally-responsive care for patients from rural communities. [ABSTRACT FROM AUTHOR]

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed. Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios. Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data. [ABSTRACT FROM AUTHOR]

This paper presents a proof-of-concept Augmented Reality (AR) system known as "SMARTLab" for safety training in hazardous material science laboratories. The paper contains an overview of the design rationale, development, methodology, and user study. Participants of the user study were domain experts (i.e., actual lab users in a material science research department, n = 13), and the evaluation used a questionnaire and free-form interview responses. The participants undertook a virtual lab experiment, designed in collaboration with a domain expert. While using the AR environment, they were accompanied by a virtual assistant. The user study provides preliminary findings about the impact of multiple dimensions, such as Performance Expectancy, Emotional Reactivity, and Spatial Presence, on SMARTLab acceptance by analyzing their influence on the Behavioral Intention dimension. The findings indicate that users find the approach useful and that they would consider using such a system. Quantitative and qualitative analysis of the SMARTLab assessment data suggests that a) AR-based training is a potential solution for laboratory safety training without the risk of real-world hazards, b) realism remains an important property for some aspects such as fluid dynamics and experimental procedure, and c) use of a virtual assistant is welcome and provides no sense of discomfort or unease. Furthermore, the study recommends the use of AR assistance tools (a virtual assistant, an attention funnel, and an in-situ arrow) to improve usability and make the training experience more user-friendly. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

As global risks and disasters increase, resilience is needed to cope with risks and uncertainties. Cities are the subject of a large amount of resilience research. Hence resilient urban development covers many areas. Theoretical resilience research either focuses on a special field or extends over the context of a single analysis and evaluation system. There is a lack of multi-perspective comprehensive analysis and evaluation methods in the existing research. This paper reviews different theoretical frameworks of resilience through an induction method, and analyzes the resilience evaluation methods by means of literature-based measurement and visual expression methods. Based on the theoretical frameworks, this paper develops a model integrating different resilience analysis types and extends it to four interrelated dimensions in order to visualize the correspondence between the analysis frameworks and the evaluation methods. Finally, the paper develops a comprehensive approach to resilience analysis and evaluation, which is of great significance for understanding how resilient cities operate. [ABSTRACT FROM AUTHOR]

In recent years, the applications of digital humans have become increasingly widespread. One of the most challenging core technologies is the generation of highly realistic and automated 3D facial animation that combines facial movements and speech. The single-modal 3D facial animation driven by speech typically ignores the weak correlation between speech and upper facial movements as well as head posture. In contrast, the video-driven approach can perfectly solve the posture problem while obtaining natural expressions. However, mapping 2D facial information to 3D facial information may lead to information loss, which make lip synchronization generated by video-driven methods is not as good as the speech-driven methods trained on 4D facial data. Therefore, this paper proposes a dual-modal generation method that uses speech and video information to generate more natural and vivid 3D facial animation. Specifically, the lip movements related to speech are generated by speech-video information, while speech-uncorrelated postures and expressions are generated solely by video information. The speech-driven module is used to extract speech features, and its output lip animation is then used as the foundation for facial animation. The expression and pose module is used to extract temporal visual features for regressing expression and head posture parameters. We fuse speech and video features to obtain chin posture parameters related to lip movements, and use these parameters to fine-tune the lip animation generated form the speech-driven module. This paper introduces multiple consistency losses to enhance the network's capability to generate expressions and postures. Experiments conducted on the LRS3, TCD-TIMIT and MEAD datasets show that the proposed method achieves better performance on evaluation metrics such as CER, WER, VER and VWER than the current state-of-the-art methods. In addition, a perceptual user study show that over 77% and 70% of cases believe that this paper's method is better than the comparative algorithms EMOCA and SPECTRE in terms of realism. In terms of lip synchronization, it received over 79% and 66% of cases support, respectively. Both evaluation methods demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recent technological advancements are transforming the architecture, engineering and construction (AEC) industry, focusing on digitalization and automation opportunities as drivers for improvement. In a realm as complex as building permitting, the drive for automation should be anchored in a comprehensive understanding of the process it seeks to transform. Furthermore, analyzing the process from a global perspective is essential for identifying opportunities for improvement. This article addresses the need for a systematic and robust approach to evaluate and compare building permit processes globally. Traditional approaches for process comparisons fail to capture the intricacies of a building permit process. Hence, in this paper, the authors propose and introduce the Process Analysis and Comparative Evaluation methodology, PACE-BP, which was tailored for building permit processes. The proposed method relies on a previously developed framework for process mapping which provides a comparable data set of building permit process maps in different countries. The comparative evaluation is then performed by assessing the maps based on predefined indicators, which are used to compile a representative vector for each process. The computational method for compiling the vectors and calculating the similarity measures between pairs of processes is described in detail. The application of PACE-BP for analyzing and comparing building permit processes in Germany, the US, Israel, and Italy is demonstrated in this paper. Our findings challenge the pursuit of a universal "best practice," emphasizing the need to balance priorities and stakeholder's objectives. The PACE-BP method is a powerful tool for policymakers and stakeholders, providing meaningful insight into existing processes and pinpointing drawbacks and limitations in current practices, while revealing opportunities for improvement. [ABSTRACT FROM AUTHOR]

During civil aviation flights, the aircraft needs to accurately monitor the real-time navigation capability and determine whether the onboard navigation system performance meets the required navigation performance (RNP). The airborne flight management system (FMS) uses actual navigation performance (ANP) to quantitatively calculate the uncertainty of aircraft position estimation, and its evaluation accuracy is highly dependent on the position estimation covariance matrix (PECM) provided by the airborne integrated navigation system. This paper proposed an adaptive PECM estimation method based on variational Bayes (VB) to solve the problem of ANP misevaluation, which is caused by the traditional simple ANP model failing to accurately estimate PECM under unknown time-varying noise. Combined with the 3D ANP model proposed in this paper, the accuracy of ANP evaluation can be significantly improved. This enhancement contributes to ensured navigation integrity and operational safety during civil flight. [ABSTRACT FROM AUTHOR]

Aviation networks exhibit multi-layer characteristics and heterogeneity of nodes and edges. Identifying key nodes in a multi-layer heterogeneous aviation network (MHAN) and elucidating its cascading failure process are of great practical significance for enhancing the ability to resist failure and strengthening network resilience. Therefore, this paper first establishes the basic model of MHAN and then designs an intra-layer node importance evaluation method based on the improved TOPSIS-grey correlation analysis (ITG) method and an inter-layer influence weight quantification method based on the gravity model. By integrating the information transmission characteristics between network nodes, a key node identification method in MHAN through aggregating multi-source information is proposed. Finally, based on the actual overload operation of aviation networks, the initial load adjustment coefficient, capacity limit, and overload coefficient are introduced based on the traditional capacity–load model, a cascading failure model of MHAN considering overload condition and failure probability is constructed, and a node influence index based on this model is defined to verify the accuracy of the key node identification results. The instance analysis conducted on the aviation network in western China demonstrates that the intra-layer node importance evaluation method based on ITG yields results with better distinguishability and higher accuracy. The key nodes are predominantly hub en-route nodes and sector nodes. In the cascading failure model, increasing the overload coefficient and capacity limit within a specific range while reducing the initial load adjustment coefficient helps reduce the network failure scale. The key nodes identified by the node influence index are consistent with those identified by the method proposed in this paper, validating the accuracy and effectiveness of the key node identification method in MHAN through aggregating multi-source information herein. [ABSTRACT FROM AUTHOR]

Hot embossing is a manufacturing technique used to create microchannels on polymer substrates. In recent years, microchannel fabrication technology based on hot embossing has attracted considerable attention due to its convenience and low cost. A new evaluation method of microchannels, as well as an approach to obtaining optimal hot embossing conditions based on the Taguchi method, is proposed in this paper to fabricate precise microchannels for a flexible proton-exchange membrane fuel cell (PEMFC). Our self-made hot embossing system can be used to fabricate assorted types of micro-channel structures on polymer substrates according to various applications, whose bottom width, top width, height and cross-sectional area vary in the aims of different situations. In order to obtain a high effective filling ratio, a new evaluation method is presented based on the four parameters of channel structures, and the Taguchi method is utilized to arrange three main factors (temperature, force and time) affecting the hot embossing in orthogonal arrays, quickly finding the optimal condition for the embossing process. The evaluation method for microchannels proposed in this paper, compared to traditional evaluation methods, incorporates the area factor, providing a more comprehensive assessment of the fabrication completeness of the microchannels. Additionally, it allows for the quick and simple identification of optimal conditions. The experimental results indicate that after determining the optimal embossing temperature, pressure and time using the Taguchi method, the effective filling rate remains above 95%, thereby enhancing the power density. Through variance analysis, it was found that temperature is the most significant factor affecting the hot embossing of microchannels. The high filling rate makes the process suitable for PEMFCs. The results demonstrate that under optimized process conditions, a self-made hot embossing system can effectively fabricate columnar structure microchannels for PEMFCs. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm. [ABSTRACT FROM AUTHOR]

Foundation scour is the erosion of sediments around pile foundations by wave and current in offshore wind energy. This phenomenon destabilizes foundations and poses a threat to pile safety. Therefore, scour protection becomes a crucial challenge in offshore wind projects. This paper reviews and synthesizes recent publications and patented technologies related to scour protection. Considering the primary engineering concerns, the paper proposes design principles for effective scour protection schemes to standardize evaluation criteria. These principles prioritize efficacy, independence, and cost-efficiency, enabling the analysis of scour protection scheme applicability. In addition, this paper summarizes and describes common protection schemes in the literature. The effectiveness of their protection is analyzed and summarized, and their economic and performance independence is evaluated. This paper categorizes flow-altering scour protection schemes found in the literature. Based on a comprehensive understanding of the mechanisms and engineering requirements of scour protection, the paper proposes a focus on determining the erosion reduction rate curve ( E p − U / U c curve) as a key criterion for evaluating the effectiveness of protection schemes under varying flow velocities and the erosion reduction rate of scour protection schemes under extreme conditions. The study highlights the necessity of establishing a comprehensive design evaluation methodology, which is crucial for addressing the significant challenges related to scour encountered in offshore wind power projects. [ABSTRACT FROM AUTHOR]

This article focuses on the cascading failure problem and node importance evaluation method in complex networks. To address the issue of identifying important nodes in dynamic networks, the method used in static networks is introduced and the necessity of re-evaluating node status during node removal is proposed. Studies have found that the methods for identifying dynamic and static network nodes are two different directions, and most literature only uses dynamic methods to verify static methods. Therefore, it is necessary to find suitable node evaluation methods for dynamic networks. Based on this, this article proposes a method that integrates local and global correlation properties. In terms of global features, we introduce an improved k-shell method with fusion degree to improve the resolution of node ranking. In terms of local features, we introduce Solton factor and structure hole factor improved by INCC (improved network constraint coefficient), which effectively improves the algorithm's ability to identify the relationship between adjacent nodes. Through comparison with existing methods, it is found that the KPDN–INCC method proposed in this paper complements the KPDN method and can accurately identify important nodes, thereby helping to quickly disintegrate the network. Finally, the effectiveness of the proposed method in identifying important nodes in a small-world network with a random parameter less than 0.4 was verified through artificial network experiments. [ABSTRACT FROM AUTHOR]