Entropy-based Indicator Research Articles

Assessment of environmental sustainability with entropy-based indicators for integrated buildings

Assessment of environmental sustainability with entropy-based indicators for integrated buildings

Assessment of environmental sustainability with entropy-based indicators for integrated buildings

Different climatic conditions directly affect fuel consumption of traditional heating systems in buildings and, together with this system-induced entropy generation, constitute one of the main sources of CO2 emissions with a global impact. Although these consumptions vary primarily according to combustion performance, they generally define a high rate of exergy destruction due to irreversibility depending on the system operation and load values. In this study, first, exergy analyses were performed in order to evaluate the entropy performance and analyse it comparatively with the process efficiency. Accordingly, the fuel-based exergy efficiency of the building was found to be 55.02%, and the efficiency potential for total improvement was determined as 20.31% depending on the parametric values and the entropy criteria developed. At the end of the study, the features of the two criteria developed in such building preferences were evaluated and some recommendations were presented.

Intrinsic sustainability assessment at technosphere level by adapting entropy based ecologic indicators. A preliminary analysis for some main waste treatment processes.

The present study was developed on the following evidence: "what is considered to be environmentally sustainable in the technosphere has to be in compliance with the development tendency of the ecosphere". Starting from this hypothesis, two entropy-based indicators were proposed: the specific entropy per amount of exergy gained (SEEG); the specific entropy per amount of exergy exploitable in the technosphere (SEEE). These were retrieved from the specific entropy (SE) indicator largely exploited for assessing the health status and the development tendency of ecosystems. Entropy based indicators SEEG and SEEE were used for assessing the environmental sustainability of incineration, anaerobic digestion and composting of waste. The aim of these indicators was to support already available methodologies for environmental impact assessment for better understating the intrinsic sustainability of specific processes, since the only quantification of the emission appears not enough informative for this goal. Concerning the ecosphere, SE minimization follows the Darwinian principle "survival of the fittest", being the fittest ecosystems those able to make the most efficient and effective use and storage of energy and materials, at the lower relative entropy generation. Consequently, the lower are SEEG and SEEE the fittest is the analysed system (i.e. sustainable). Main findings indicate SEEG ranging from 2.471 to 3.705, for incinerator, from 0.007 to 0.106 for anaerobic digestion whereas resulted constant = 0.266 for composting. The SEEE ranged from 0.433 to 0.484 for incinerator, from <0.001 to 0.008 for anaerobic digestion and resulted constant = 0.014 for composting. These preliminary results indicates that processes and system able to return chemicals and materials exploitable at technosphere level were characterized by higher ability in returning exergy flows at the lower entropy increase compared to hose returning only energy.

A two-stage fault diagnosis methodology for rotating machinery combining optimized support vector data description and optimized support vector machine

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal samples and fault samples as equally important for pattern recognition training. It ignores that the rotating machinery is in a normal state most of the time, and the collected normal samples are far more than the fault samples. Considering this situation, this paper proposes a two-stage intelligent fault diagnosis methodology for rotating machinery combing optimized support vector data description (SVDD) and optimized support vector machine (SVM). Specifically, SVDD is applied for fault detection, and SVM is applied for fault identification. The parameters of SVDD and SVM are optimized by the grasshopper optimization algorithm (GOA). Multiscale entropy (ME) is used for feature extraction, which is the input feature vector of SVDD and SVM. Under the framework of the proposed methodology, the strengths and weaknesses of 8 different entropy-based indicators in feature extraction are explored. The analysis results of three cases prove the availability and universality of the proposed methodology. According to the analysis results of three case analyses, the permutation entropy (PE)-based indicators are not promising for fault detection. Refined composite multiscale fuzzy entropy (RCMFE) is recommended for fault detection and fault identification to generalize the universality. Based on RCMFE, the fault detection accuracy in the three cases is more than 99%, and the fault identification accuracy is more than 94%. This methodology, therefore, provides a powerful tool for rotating machinery fault diagnosis, which is beneficial to practical application.

Sustainable development goals research in higher education institutions: An interdisciplinarity assessment through an entropy-based indicator

Since 2015, the United Nations has urged higher education institutions (HEIs) to adopt an interdisciplinary approach towards the Sustainable Development Goals (SDGs). In other words, universities are encouraged to transcend any single disciplinary perspective in exploring sustainable development issues. This study examines the importance of driving the scientific production of HEIs towards the SDGs as a concrete institutional contribution to sustainable development. While bibliometric tools for the SDGs are currently emerging, the existing models have not focused on interdisciplinarity or on their usefulness as decision-management tools to drive SDG-related research at a micro-scale (i.e. the institutional level). This study proposes a novel multi-step methodology. It develops an initial case study, which applies information system design theory (ISDT) to map and assess interdisciplinary research into each SDG at an Italian generalist university (University of Turin). Utilising a quantitative text analysis, we examined a database containing over 30,000 entries representing the university’s SDG-related scientific production from 2015 to 2019. Subsequently, we conducted a social network analysis (SNA) of co-authorship to measure interdisciplinarity for each SDG. We defined interdisciplinarity as collaboration among researchers in diverse disciplines. We employed a modularity algorithm to select bottom-up clusters of researchers from diverse departments. Finally, we analysed the identified clusters to propose an Interdisciplinarity Sustainability Index (ISI) capable of identifying the most investigated and interdisciplinary SDGs. Ultimately, our results enable the quantification of interdisciplinary SDG research via the proposed methodology. The study helps to visualise inter-departmental collaborations and thus informs university managers’ efforts to identify and coordinate compatible research groups to bridge inter-organisational boundaries.

Convergence diagnostics for Monte Carlo fission source distributions using the Wasserstein distance measure

The power iteration technique is commonly used in Monte Carlo (MC) criticality simulations to obtain converged neutron source distributions. Entropy is a typical indicator used to examine source distribution convergence. However, spatial meshing is required to calculate entropy, and the performance of a convergence diagnostic is sensitive to the chosen meshing scheme. A new indicator based on the Wasserstein distance (WD) measure is proposed; it is intuitive and easy to implement in MC codes. In addition, the usage of the WD-based indicator is almost input-free, which not only lowers the thresholds that users must meet but also reduces modeling errors. Numerical tests are conducted with typical source convergence problems and a full-core problem. The results show that the WD-based indicator is more conservative and reliable than the existing entropy-based indicators. For the BEAVRS problem, the average relative errors of the axial fission rate obtained using the entropy-based indicator are larger than 4%, while it is less than 1% when using the WD-based indicator. An extra computational cost is required to calculate the WD values, but the performance penalty is acceptable, with the slowdown less than 2% for large-scale problems.

Design-eligibility study of solar thermal helically coiled heat exchanging system with annular dimples by irreversibility concept

This is a numerical research that aims to design several helically grooved coils for solar water heating systems and compare them based on the irreversibility concept. To this end, helical coils with circle, square, horizontal elongated circle (HEC), and vertical elongated circle (VEC) cross-sections were annularly grooved for enhancing heat transfer rate. The Second law analysis in this study was performed based on several variables including flow rate (1–6 lit/min), geometrical, temperature (283–323K), and type of working fluid. Depth and distance of grooves were considered as geometrical parameters. Water, EG (CH2OH)2 (IUPAC name: ethane-1,2-diol), and Theminol-55 were used as heat transfer fluids. Selected fluids make a Pr range of 3.35–744. Various entropy-based indicators were used to select the most optimum condition. Moreover, the interactions between triple thermodynamic domains (energy, entropy, and exergy) were conceptualized. Results demonstrated that for all cases the highest entropy generation can be achieved in the higher flow rates (6 lit/min), also when groove depth and distance are, respectively, 1.3 mm and 20 mm. Case ''VEC'' has the lowest ratio of grooved dimensionless entropy to un-grooved dimensionless entropy.

MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy.

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.

Using technological entropy to identify technology life cycle

Abstract Identification of technology life cycles(TLC) provides a crucial basis for managing national policy, regional planning, and enterprise investment. Thus, it is a significant challenge to determine the stages of TLC. To this end, an entropy-based indicator is proposed, as well as a quantitative method based on the S-curve of entropy is established to identify the stages of TLC. Furthermore, the effectiveness of the method is verified by the analogy of three typical cases (thin-film-transistor liquid-crystal displays, cathode ray tubes, and nano-biosensors). It is clear that the entropy calculation produces a sum of overall distributions for patent applications against the researchers in the field to be studied, which can be used to find out the stage changes of TLC, while the other analysis considers trends of many patent active measures such as patent applications and citations collectively, to figure out the changes. Thus, the former constructs an index that has clear meanings and then uses its characterization to identify the changes logically, while the latter can only try to identify them empirically often with no trivial difficulties as these trends are often inconsistent. Finally, three-dimensional (3D) printing is investigated as an empirical case study, which reveals that 3D printing is still in its growth stage.

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

Adaptive grayscale image coding scheme based on dynamic multi-grouping absolute moment block truncation coding

Multi-Grouping Absolute Moment Block Truncation Coding (MGAMBTC) technique improve the image quality of Absolute Moment Block Truncation Coding (AMBTC) by adaptively dividing pixels in a block into groups according to block activity. This study found that the bit rate can be reduced further if there is some similarity among the blocks. Therefore, this study proposes a block prediction scheme that exploits the intra-block similarity of neighboring blocks. If a similar encoded block is found, its position code will be stored to encode the block; otherwise, it is encoded with the proposed improved MGAMBTC (iMGAMBTC). The proposed iMGAMBTC employs an entropy-based indicator generation mechanism to reduce the bit rate, and the evaluation demonstrates that the proposed scheme enhances the compression performance of AMBTC and MGAMBTC efficiently.

Chatter detection in milling processes using frequency-domain Rényi entropy

Chatter is a kind of self-excited vibration and causes negative effects in machining processes. This paper presents a practical method to identify the chatter with cutting force signals in milling processes. Since the spectrum of the chatter signal exhibits discrete spectral lines around the chatter frequencies and the Renyi entropy is an effective index to characterize the randomness of data series, the frequency-domain Renyi entropy is proposed as a chatter indicator. As the chatter severity level grows, the signal components at the chatter frequencies become more and more significant, which means a reduction of the randomness of the spectral series. As a result, the value of the Renyi entropy-based indicator decreases rapidly at the onset of the chatter. In order to eliminate the interference of the normal signal components, i.e., the spindle speed-related frequency components, the spectrum is preprocessed to filter out those components first. Various milling experiments are conducted. The results show that the value of the proposed indicator changes sharply at the onset of chatter in various milling conditions with different spindle speeds and cutting depths. Also, the proposed indicator is compared with the commonly used Shannon entropy-based indicator and verified to have a larger difference between the stable and chatter statuses and is higher sensitivity to the chatter.

How Are Various Surrogate Indicators Consistent with Mechanical Reliability of Water Distribution Systems: From a Perspective of Many-Objective Optimization

Exploring the trade-off between cost and system reliability of water distribution systems (WDSs) has been focused for two decades. Due to the intensive computation associated with the reliability analysis, it is popular in the research community to replace this procedure by using surrogate indicators. However, the discussion on the correlation among different types of such indicators is generally lacking, which implies that a deeper understanding of this aspect is needed. This paper proposes a novel methodology of investigating the relationships among many commonly used surrogate indicators for measuring the mechanical reliability of WDSs. In particular, the optimal design of WDSs is formulated as a many-objective optimization problem, using cost and each surrogate indicator as an individual goal. Two benchmark design problems of different scales and complexities are considered for verifying the proposed method. The well-known multi-objective evolutionary algorithm (MOEA), namely Borg that is suitable for coping with problems involving many objectives, is used to obtain the best approximation to the Pareto-optimal fronts for both cases. Afterward, the one-pipe burst testing is conducted to quantify the correlation between mechanical reliability and surrogate indicators. Results suggest that investigating the correlation of surrogate indicators from the perspective of many-objective optimization provides a direct and efficient way of distinguishing better indicators from worse ones. Resilience-based surrogate indicators and the Redundancy indicator that only depends on nodal pressures are highly related to the mechanical reliability of WDSs. In contrast, entropy-based indicators exhibit poor performance in reflecting the mechanical reliability. These insights contribute to the selection of more appropriate surrogate indicators for the optimal design of WDSs for researchers and practitioners.

Spectral entropy of acceleration data for damage detection in masonry buildings affected by seismic sequences

Abstract One of the main drawbacks of using entropy-based indicators for damage detection is known to be their sensitivity to the energy introduced into the system. Indeed, energy supply can lead to a more deterministic behavior of the structure and thus to a reduction of the entropy. As a solution to these issues, in this paper an indicator based on two measures of spectral entropy is proposed to assess the occurrence of damage in masonry buildings, even in the presence of an external unmeasured input (e.g. minor seismic event). The proposed indicator was first validated by using a benchmark numerical model. Subsequently, the methodology was applied to dynamic response data from three monitored masonry buildings that were affected by the 2016–17 seismic sequence in Central Italy.

Specialization and multidisciplinarity of scholarly book publishers: differences between Spanish University Presses and other scholarly publishers

In this work, the authors propose a definition of book publishers’ specialization-multidisciplinarity and apply the operative definition to a set of 1952 Spanish publishers in 16 fields of the Social Sciences and the Humanities. The definition of specialization-multidisciplinarity is made operative with an entropy-based indicator. The indicator was applied to a set of Spanish scholarly publishers. It was hypothesized that University Presses (UPs hereafter) were significantly more multidisciplinary (or less specialized) than the rest of the publishers according to the values of the indicator. Chi square tests show that the proportion of UPs among the most multidisciplinary publishers is significantly greater than randomly expected. For the settings of this study the hypothesis is confirmed with a p value < 0.01. At the level of discipline, the concentration of titles was studied using Gini Coefficient, finding differences between disciplines within the range of 0.5. The specific features of university presses are discussed as possible explanations for the observed differences in thematic specialization.

Emerging urban centrality: An entropy-based indicator of polycentric development and economic growth

This study moves in the debate on polycentric development in Europe proposing an entropy-based indicator of urban centrality based on local-scale diversity in soil sealing levels. This indicator reflects a progressive transition from a mono-centric and hyper-compact morphology towards polycentric agglomerations, with formation (or consolidation) of sub-centres structurally and functionally distinct from central cities. The proposed approach was tested in three metropolitan regions (Barcelona, Rome and Athens) with the aim to provide a comparative analysis of recent urbanization patterns in European countries affected by rapid processes of urban spillover. The local socioeconomic structure underlying urban expansion was investigated using descriptive, correlation and multivariate analysis of 52 contextual indicators. Results of this study shed light on long-term urbanization patterns in the examined cities, providing evidence of a progressive settlement scattering in Rome and Athens. Early signals of polycentrism were observed in Barcelona. Diversification in the level of soil sealing decreased with the distance from central cities. The article finally debates on the use of composite indexes of diversification in the level of soil sealing when assessing polycentric urban development.

Application of VIX and entropy indicators for portfolio rotation strategies

In our study, we use sample entropy and approximate entropy indicators − derived from the India Volatility Index (India VIX) − to explore the feasibility of style, size and time horizon-based portfolio rotation strategies. We show that these two entropy-based indicators are significantly and strongly related to portfolio rotation strategy based on style and size than the trading strategies based on signals derived from percentage change in India VIX. Finally, the comparative portfolio performances show that the trading strategies based on sample entropy outperform those based on VIX change. These results provide evidence of the prospect for new investment and diversification strategies in the otherwise less-studied emerging markets.

An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation

How to evaluate the value of a patent in technological innovation quantitatively and systematically challenges bibliometrics. Traditional indicator systems and weighting approaches mostly lead to moderation results; that is, patents ranked to a top list can have only good-looking values on all indicators rather than distinctive performances in certain individual indicators. Orienting patents authorized by the United States Patent and Trademark Office (USPTO), this paper constructs an entropy-based indicator system to measure their potential in technological innovation. Shannon's entropy is introduced to quantitatively weight indicators and a collaborative filtering technique is used to iteratively remove negative patents. What remains is a small set of positive patents with potential in technological innovation as the output. A case study with 28,509 USPTO-authorized patents with Chinese assignees, covering the period from 1976 to 2014, demonstrates the feasibility and reliability of this method.

Disciplinarity and interdisciplinarity in citation and reference dimensions: knowledge importation and exportation taxonomy of journals

This work proposes an entropy-based disciplinarity indicator (EBDI) which allows the classification of scientific journals in four classes: knowledge importer, knowledge exporter, disciplinary and interdisciplinary with regards to the discipline(s) in which they are classified. Assuming that the set references in the papers published in a journal represent a significant part of their knowledge basis, the diversity (measured with Shannon's entropy) and ratio between internal and external (to the discipline in which the journal is classified) references can provide a measure of the disciplinarity/interdisciplinarity of the journal in the reference dimension. The homologous analysis can be applied to the set of citations received by the papers published in the journal. In this article, an entropy-based indicator for the measurement of the disciplinarity of scientific journals is developed, applied (to the cited and citing dimensions) and discussed. The indicator can take finite values and it is found to be theoretically consistent when tested against two definitions for bibliometric indicators. The combinations of disciplinarity values in the citing and cited dimensions permits the classification of journals according to their knowledge importing/exporting profile (separately, with regards to the social sciences or the sciences), providing a taxonomy of the role of journals according to their importing, exporting, interdisciplinary or specialized profile with regards to the subject category in which they are classified. The indicator, EBDI and the resulting taxonomy is proposed and tested for the set of journals in LIS subject category in JCR 2013 and for the sets of journals in Andrology and Legal Medicine in JCR 2015. Evidence of concurrent validity with journal co-classification patterns is found in the three sets of journals.

Towards a sounder interpretation of entropy-based indicators in ecological network analysis

Various indicators rooted in the concepts of information and entropy have been proposed to be used for ecological network analysis. They are theoretically well grounded and widely used in the literature, but have always been difficult to interpret due to an apparent lack of strict relations with node and link weight. We generated several sets of 10,000 networks in order to explore such relations and work towards a sounder interpretation. The indices we explored are based on network composition (i.e., type and importance of network compartments), or network flows (i.e., type and importance of flows among compartments), including Structural Information (SI), Total System Throughput (TST), Average Mutual Information (AMI), Flow Diversity (H), and Ascendency (ASC). A correlation analysis revealed a lack of strict relationships among the responses of the investigated indicators within the simulated space of variability of the networks. However, fairly coherent patterns of response were revealed when networks were sorted by following a “bottom-up” criterion, i.e. by increasing the dominance of the large-sized top predator in the network. This ranking is reminiscent of ecosystem succession, along which the prominence of higher trophic level organisms progressively increases. In particular, the results show that a simple increase in organisms having large size and low consumption rates is potentially able to simultaneously lead to an increase of different types of information (as SI, H and AMI), thus also emphasizing the importance of bionomic traits related to body size in affecting information-related properties in a trophically connected community. The observed trends suffer from a certain dispersion of data, which was diminished by imposing specific and ecologically meaningful constraints, such as mass balancing and restriction to certain range of the ratio A/C, an index related to the viability of ecological networks. These results suggest that the identification of a set of effective constraints may help to identify improved conditions for applicability of the investigated flow-based indicators, and also provide indication on how to normalise them with respect to meaningful network properties or reference states. Thus, in order to increase confidence in the derived network metrics describing a particular ecosystem state, and thus increase their applicability, it is advisable to construct replicate networks by taking the variability of input data into account, and by applying uncertainty and sensitivity analyses.