Entropy Index Research Articles

Shaping the general resilience of green infrastructure through integrating structures, functions, and connections

Global climate change has necessitated the implementation of green infrastructure that is resilient in a manner of sustainable development. The current understanding of green infrastructure resilience is hindered by the divergence of generic properties and performance in adapting to uncertain disturbances. This study develops an operational methodology that integrates structural and functional properties of green infrastructure, and their connections to shape the general resilience. A further empirical study is conducted in the context of Shenzhen City, where the effectiveness of resilient connections is correlated with the distribution of waterlogging. We demonstrate that green infrastructure present different levels of resilience in terms of its structural composition and functional performance. The Shenzhen city shows a high capacity to maintain soil retention stability, but a feeble capacity regarding water yield and gross primary productivity. The resilient connections of green infrastructure are highly centralized, with a few pivotal nodes performing a high degree of connectivity. It shows that a total of 52.2% of resilient lines are identified as belonging to the fourth level but linking the majority of the nodes. Enhancing the general resilience of green infrastructure could facilitate its adaptation to specific disturbances such as waterlogging. When correlated the resilient connections of green infrastructure with the distribution of waterlogging, a distance of 1.6 km from the waterlogging points is significantly identified where the residuals of the entropy index display the lowest variance. As the distance increases, the composite entropy index initially decreases and then increases. We suggest that the alignment of generic properties and specified performance of green infrastructure is essential in the pursuit of sustainable development.

High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

A robust ensemble of hybrid and bivariate statistical models for flood prediction mapping in Lower Damodar River Basin of India

This research explores flood prediction in the Lower Damodar River Basin (LDRB) using a hybrid ensemble of a Naïve Bayes Tree (NBT) and five bivariate statistical models such as Evidential Belief Function (EBF), Index of Entropy (IOE), Frequency Ratio (FR), Statistical Index (SI), and Modified Information Value (MIV). A total of 348 flood locations and 15 conditioning factors including hydrological, topographical and land cover were considered for this analysis. To ensure the precision of model predictions, a multicollinearity assessment was executed. Receiver operating characteristic (ROC) curve, area under curve (AUC), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were performed to compare and asses each of the models all. The results reveal that all models performed well in creating flood hazard maps with AUC >0.8 and RMSE <0.4. FR and EBF models demonstrated the highest predictive accuracy (AUC=0.85), followed by the IOE, SI, and MIV models. The ensemble of NBT with bivariate models shows promising results, showcasing reduced error metrics and improved accuracy for the IOE, SI, and MIV models. This study highlights the potential of ensemble models in flood hazard prediction, offering valuable insights for global flood risk management. The successful application of these data-driven models showcases their importance in forecasting flood risks, aiding decision-makers and planners in developing more effective flood mitigation strategies.

Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India

Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India

A two-step bearing fault diagnosis strategy under variable speed based on symplectic geometry modal decomposition and practical fault feature extraction framework

Abstract Strong noise interference can lead to failure of bearing fault diagnosis techniques. This paper proposes a two-step fault diagnosis strategy to address the challenge of weak fault feature extraction in bearing fault diagnosis using acoustic or vibration data at varying speed. Firstly, the paper introduces a short-time symplectic modal decomposition (stSGMD) method that utilizes fractional Fourier transform. This method involves signal processing with short-time windowing to extract fault-sensitive components. The window is then expanded to obtain the complete component through fractional Fourier narrow-band filtering based on energy concentration in the fractional Fourier domain. A novel entropy index, standard deviation discrete entropy, is introduced to quantify the intensity of fault shocks in non-stationary signal and is used to select components in the stSGMD. Subsequently, a fault feature extraction framework called global objective deconvolution (GOD) is presented for extracting instantaneous fault features at varying speed. This method establishes a global objective matrix for the extraction process. The GOD is utilized to deconvolute the complete fault-sensitive component, followed by envelope order analysis for demodulating the fault feature order. Numerical simulations and experimental studies on acoustics and vibration are performed. The results demonstrate that stSGMD improves the demodulation capability of SGMD, while GOD effectively extracts fault features. It is expected that the presented method will be effectively utilized for fault feature extractions in bearings operating under linear variable speed conditions.

Antibiotic Diversity Index: A novel metric to assess antibiotic variation among hospitalized children.

Despite nationally endorsed treatment guidelines and stewardship programs, variation and deviation from evidence-based antibiotic prescribing occur, contributing to inappropriate use and medication-related adverse events. Measures of antibiotic prescribing variability can aid in quantifying this problem but are not adequate. The objective of this study is to develop a standardized metric to quantify antibiotic prescribing variability (diversity) within and across children's hospitals, and to examine its association with outcomes. We performed a cross-sectional study of empiric antibiotic exposure among children hospitalized during 2017-2019 with one of 15 common pediatric infections using the Pediatric Health Information System database. Encounters for children with complex chronic conditions, transfers in, and birth hospitalizations were excluded. Using the Shannon-Weiner entropy index, we quantified antibiotic diversity for each infection type using the d-measure of diversity. Generalized linear mixed-effects models were used to examine the association between hospital-level antibiotic diversity and risk-adjusted length of stay and costs. A total of 79,515 hospitalizations for common pediatric infections were included. Antibiotic diversity varied within and across hospitals. Infections with low mean antibiotic diversity included appendicitis (mean diversity [mDiv] = 4.9, SD = 2.5) and deep neck space infections (mDiv = 5.9, SD = 1.9). Infections with high mean antibiotic diversity included pneumonia (mDiv = 23.4, SD = 5.6) and septicemia/bacteremia (mDiv = 28.5, SD = 12.1). There was no statistically significant association between hospital-level antibiotic diversity and risk-adjusted LOS or costs. We developed and applied a novel metric to quantify diversity in antibiotic prescribing that permits comparisons across hospitals and can be leveraged to identify high-priority areas for local and national stewardship interventions.

IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA

Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considerations. This research compares the FCM and FPCM algorithms using local poverty data from Indonesia, specifically examining the Partition Entropy (PE) index value. It aims to identify the optimal number of clusters for provincial-level poverty data in Indonesia. The findings indicate that the FPCM algorithm outperforms the FCM algorithm in categorizing poverty in Indonesia, as evidenced by the PE validity index. Furthermore, the study identifies that the ideal number of clusters for the data is 2.

Fuzzy C-Means Algorithm Modification Based on Distance Measurement for River Water Quality

River water quality could be determined by understanding the capacity of pollutants in a water body. Fuzzy C-Means (FCM) is one of the fuzzy clustering methods for determining river water quality by measuring water quality parameters, that is, dissolved oxygen (DO) and total dissolved solids (TDS). The FCM algorithm is an effective fuzzy clustering algorithm for grouping data but often produces local and inconsistent optimal solutions due to the partition matrix's random initialisation process. Therefore, this study proposes to modify the FCM algorithm to be precise in the partition matrix initialisation process using several distance concepts. The purpose of the proposed algorithm modification is to get more consistent FCM clustering results and minimise stop iterations. The validation process for the clustering results uses the FCM algorithm, and the FCM modification algorithm uses three parameters, namely the Partition Coefficient Index (PCI), Partition Entropy Index (PEI) and Silhouette Score (SS). The experiments were conducted with three replications and using various distance concepts. The results showed that the number of iterations stopped in the FCM algorithm has different values for PCI, PEI, SS, and stop iterations and objective functions in each trial. On the contrary, the FCM modification algorithm has consistent PCI, PEI, and SS values, and the number of iterations stops with fewer iterations. Therefore, the modified algorithm for initialising the partition matrix can be used in the fuzzy C-means clustering algorithm.

A Regression-based Method for Estimating Generalised Entropy and Atkinson Inequality Indices and their Standard Errors

Abstract In this paper we show that a regression-based approach can be used to estimate generalised entropy and Atkinson inequality indices and their associated standard errors. The applicability of this approach is demonstrated using the health expenditure data from the United States (US) medical expenditure panel survey (MEPS).

Assessing the diagnostic value of left ventricular synchrony indices derived from phase analysis by D-SPECT in identifying obstructive coronary artery disease.

This study aimed to assess the diagnostic efficacy of left ventricular synchrony (LVS) for detecting coronary artery disease (CAD). We explored whether the LVS index derived from phase analysis of D-SPECT provides superior diagnostic value compared to conventional perfusion analysis in identifying obstructive CAD. Patients with suspected or confirmed CAD underwent drug-stress/rest gated D-SPECT myocardial perfusion imaging (MPI) and coronary angiography (CAG). A 50% stenosis was set as the threshold for obstructive CAD. 110 participants were enrolled in this analysis. There were significant differences in phase standard deviation (PSD), phase histogram bandwidth (PHB) and entropy among the four groups. Patients without cardiac disease and those with mild-moderate stenosis exhibited no noticeable contraction asynchrony. However, LVS indices demonstrated a gradual increase with the progression of coronary stenosis when compared to NC (P < 0.001). Obstructive CAD was identified in 43 out of 110 participants (39%). Optimal cutoff values for diagnosing obstructive CAD during stress were determined as 7.6° for PSD, 24° for PHB, and 37% for entropy, respectively. Notably, PSD, PHB, and entropy indices exhibited higher sensitivity compared to MPI. The integration of the stress-induced LVS indices into routine MPI analysis resulted in a significantly greater area under the curve (AUC), leading to improved diagnostic performance and enhanced differential capacity. Stress-induced LVS indices increase with the severity of coronary artery stenosis by D-SPECT phase analysis. Further, the indices-derived phase analysis exhibits superior sensitivity and discriminatory ability compared to MPI in detecting obstructive CAD.

Soundscape characteristics of RAS tanks holding Atlantic salmon (Salmo salar) during feeding and feed withdrawal

Behavioural monitoring can provide crucial information on welfare and feeding in aquaculture. Passive acoustic monitoring of behaviour can be particularly useful in recirculating aquaculture systems (RAS), as they often have turbid water that impairs visual monitoring. Currently, little is known about the sounds that make up the soundscapes in RAS tanks holding Atlantic salmon (Salmo salar). In this study, hydrophones were used to continuously record the soundscape in eight single tank RAS holding Atlantic salmon parr for 15 days, with the fish in four of the tanks being fasted by feed withdrawal for five days from the sixth to the tenth day. The results show that soundscapes in RAS tanks are affected by feeding. Two main sound sources were identified during feeding in RAS tanks, one related to pellets delivery and the other to fish behaviour. The sound of pellets hitting the water surface had energy concentrated at frequencies between 1.7 and 4.0 kHz, with peak frequency decreasing and amplitude increasing with increasing number of pellets hitting simultaneously. The feeding sounds of Atlantic salmon had energy concentrated at frequencies between 6.5 and 9.4 kHz.More complex soundscapes were recorded during feeding events. These were characterized by variations in amplitude and frequency that have been described by using acoustic indexes in RAS tanks for the very first time. The Acoustic Complexity Index (ACI), the Acoustic Entropy Index (H) and the Normalized Difference Soundscape Index (NDSI) showed distinct changes in the soundscape related to feeding events; ACI increased while H and NDSI decreased compared to the times in between scheduled feeding times. The sound types identified in this study and the outcomes of the acoustic indices indicate a possibility to monitor system performance as well as fish behaviour in the tank soundscapes in RAS. Soundscape monitoring can contribute to match feeding closer to fish appetite, improve water quality, and reduce risks that deviations in the system performance can have on fish welfare during production.

Lead II electrocardiograph-derived entropy index for autonomic function assessment in type 2 diabetes mellitus

The aim of this study was to introduce and evaluate the baroreflex entropy index (BEI), a novel tool derived from standard lead II electrocardiograph (EKG) for autonomic function (AF) assessment in type 2 diabetes mellitus (T2DM). Researchers with distinct roles (analysis and data preparation) analyzed anonymized EKG data from healthy controls and two patient groups with T2DM (well controlled and poorly controlled). BEI was compared between groups, and correlations with glycemic markers (HbA1c, fasting glucose) were investigated. Logistic regression was used to assess the association between BEI and T2DM risk. BEI showed good repeatability and differentiation between groups. Notably, it required only single-lead EKG. BEI was inversely correlated with glycemic markers, suggesting improved baroreflex regulation with better glycemic control. BEI also outperformed small-scale multiscale entropy in group discrimination. Logistic regression identified BEI as a protective factor for T2DM. BEI represents a promising tool for monitoring AF, assessing glycemic control, and potentially stratifying T2DM risk. Further validation in larger longitudinal studies and an exploration of the applicability of BEI to other diseases are warranted.

Freshwater input significantly reduces specific and functional diversity of small subarctic estuaries

Estuaries face mounting anthropic pressures. Climate change is projected to increase heavy precipitations, while agricultural and logging activities reduce land permeability, leading to more frequent flooding events that lower estuarine salinity. These hyposaline conditions pose physiological challenges for marine species, potentially reducing taxonomic diversity and biomass, and ultimately, ecosystem functions and services. Polar and subpolar estuaries, historically understudied, will undergo significant changes in freshwater discharge timing and variability due to predicted shifts from snow to rainfall regimes in these high latitudes. The investigation of biotic communities structure through functional traits is crucial for understanding biodiversity changes, evaluating ecosystem tolerance, and addressing the loss of function under stressful conditions. Therefore, our study aims to characterize the taxonomic and functional diversity of hard-substrate benthic communities along a salinity gradient in small subarctic estuaries. We hypothesize that communities closer to the riverine input will be less taxonomically diverse and abundant and with smaller individuals, leading to a loss of functions due to environmental filtering. To test our hypotheses, we measured traditional (biomass, taxonomic richness and diversity) and functional (functional richness and Rao's quadratic entropy) indices, as well as observed traits, of epibenthic hard-substrate macroalgae and macroinvertebrates along a four-level salinity gradient found in five small subarctic estuaries in Quebec (Canada). As predicted, freshwater inputs induced a decrease in biomass, taxonomic richness, functional richness, and Rao's quadratic entropy, with a fivefold reduction in total biomass of the epibenthic community from marine to low salinity zones. Freshwater inputs significantly shaped both taxonomic and functional trait structures, as the proportion of suspension feeders and long-distance dispersers decreased with rising freshwater inputs. While there was no clear trend for the size-class structure of invertebrate taxa, macroalgae were smaller at low salinities. Estuarine communities in subarctic regions may be vulnerable to future alterations in flooding patterns caused by global change, potentially affecting key ecosystem services such as suspended matter filtration. Functional trait indices were found to be efficient and serve as a valuable complement to taxonomic measures in detecting community shifts along salinity gradients, highlighting their usefulness in developing effective conservation and management strategies.

Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models

ObjectiveDespite increased availability of methodologies to identify algorithmic bias, the operationalization of bias evaluation for healthcare predictive models is still limited. Therefore, this study proposes a process for bias evaluation through an empirical assessment of common hospital readmission models. The process includes selecting bias measures, interpretation, determining disparity impact and potential mitigations. MethodsThis retrospective analysis evaluated racial bias of four common models predicting 30-day unplanned readmission (i.e., LACE Index, HOSPITAL Score, and the CMS readmission measure applied as is and retrained). The models were assessed using 2.4 million adult inpatient discharges in Maryland from 2016 to 2019. Fairness metrics that are model-agnostic, easy to compute, and interpretable were implemented and apprised to select the most appropriate bias measures. The impact of changing model’s risk thresholds on these measures was further assessed to guide the selection of optimal thresholds to control and mitigate bias. ResultsFour bias measures were selected for the predictive task: zero-one-loss difference, false negative rate (FNR) parity, false positive rate (FPR) parity, and generalized entropy index. Based on these measures, the HOSPITAL score and the retrained CMS measure demonstrated the lowest racial bias. White patients showed a higher FNR while Black patients resulted in a higher FPR and zero-one-loss. As the models’ risk threshold changed, trade-offs between models’ fairness and overall performance were observed, and the assessment showed all models’ default thresholds were reasonable for balancing accuracy and bias. ConclusionsThis study proposes an Applied Framework to Assess Fairness of Predictive Models (AFAFPM) and demonstrates the process using 30-day hospital readmission model as the example. It suggests the feasibility of applying algorithmic bias assessment to determine optimized risk thresholds so that predictive models can be used more equitably and accurately. It is evident that a combination of qualitative and quantitative methods and a multidisciplinary team are necessary to identify, understand and respond to algorithm bias in real-world healthcare settings. Users should also apply multiple bias measures to ensure a more comprehensive, tailored, and balanced view. The results of bias measures, however, must be interpreted with caution and consider the larger operational, clinical, and policy context.

Plant species diversity and functional diversity relations in the degradation process of desert steppe in an arid area of northwest China

Species and functional diversity play a major role in the stability and sustainability of grassland ecosystems. However, changes in species and functional diversity during grassland degradation in arid areas as well as the underlying mechanisms remain unclear. In this study, we surveyed the vegetation and soil properties of arid regions across a degradation gradient to explore the shifts in species and functional diversity in plant communities, their relationships and key determinants during desert steppe degradation. Our results found significant variability in species diversity and functional diversity across degradation stages. Species diversity (Shannon-Wiener index (H), and Pielou index) and functional diversity (functional evenness (FEve) index, and Rao's quadratic entropy (RaoQ) index) tended to increase initially and then decrease with increasing grassland degradation. The Patrick index, Simpson index, functional richness (FRic) index, functional divergence (FDiv) index, and functional dispersion (FDis) index declined as grassland degradation increased. The relationships between species diversity and functional diversity indices at different stages of degradation in the desert steppe were inconsistent. From no to heavy degradation grasslands, the correlation between species diversity and functional diversity gradually weakened. Specifically, there was a significant correlation between Patrick (R) and FRic indices (R2 > 0.7) on both non-degraded and light degraded grasslands, but there was no significant correlation between R and FRic indices in moderately and heavily degraded grasslands (R2 < 0.7), and R2 gradually decreased. Redundancy analysis and partial least squares path modeling showed that grassland degradation has a significant direct effect on the species diversity and functional diversity. In addition grassland degradation has direct and indirect effects on the species diversity through soil available nitrogen, organic matter and total nitrogen. Functional diversity is directly or indirectly affected by species diversity, soil available nitrogen, organic matter and total nitrogen, soil moisture content, soil bulk density, and pH value. In summary, the relationship between species and functional diversity indices gradually weakened from areas with no degradation to heavy degradation in arid desert grasslands. Our study reveals the patterns and relationships between species diversity and functional diversity throughout the process of grassland degradation, demonstrating a gradual decrease in ecosystem stability and sustainability as degradation advances. Our results have significant implications for the restoration of grassland degradation and the management of ecosystem services in arid steppe regions.

Analysis of Drivers’ Workload States on Highways in High Elevation Regions

The low-pressure (and low-oxygen) environment in plateau regions affects drivers’ psychophysiological states and, thus, driving behavior and road safety. Six scenarios of expressway exit ramps were constructed based on the UC/win-road simulator to quantitatively analyze the effect of the plateau environment on workload state. A total of 50 participants in Nanjing (altitude 50 m) and 50 participants in Lhasa (altitude 3,650 m) were recruited for the simulation experiments. Based on the principal component analysis, a sample entropy index (SEI) was constructed to reflect the drivers’ psychophysiological levels by combining the sample entropy of electroencephalogram (EEG) signals β%, β/(α+θ), β/α, and heart rate (HR). Meanwhile, the pupil area change rate and the percentage of fixation time and duration within the region of interest were analyzed to characterize the drivers’ visual workload. Based on the psychophysiological and visual load, the differences in workload states among plain and plateau drivers were analyzed using a regression model and a one-way analysis of variance (ANOVA) test. At the same time, the changing trends of the drivers’ workload states in the curved section of the exit ramp under different scenarios were analyzed. The plateau drivers experienced a higher level of mental stress and greater psychological load. Furthermore, the higher radius of horizontal curves reduced the visual load level for drivers when traveling on the highway exit ramp sections. These findings should serve as a valuable reference for refining the design of plateau expressway exit ramps and providing a theoretical basis for improving driving safety in plateau areas.

Urbanization induced changes in land use dynamics and its nexus to ecosystem service values: A spatiotemporal investigation to promote sustainable urban growth

Proper knowledge regarding the spatial dependence of ecosystem service values (ESVs) on the changes in land use dynamics is essential for environmental management as well as sustainable urban development. To investigate this complex association concerning a fast-growing secondary city, this study estimated the integrated land use dynamic degree (ILUDD), uniform annual land use transition intensity (Wtn), and entropy index of land use mix (LUM), and the ESVs for Raiganj urban agglomeration (UA). The overall association between the three indices and the ESVs is explored using the spatial lag and spatial error model. For the local level association, the geographically weighted regression (GWR) is applied. The output showed that changes in ILUDD and LUM indicate over −20 unit and −2 unit changes in the ESVs of built-up with 1 unit increase in ILUDD and LUM, respectively. However, the ILUDD coefficient score concerning vegetation ESV was observed around +25. The Wtn also represented a significant loss of natural vegetation from every administrative unit of Raiganj UA. The relation between urban growth type and landscape patches is analyzed using Getis-Ord-Gi. It is found that the promotion of compact growth can be the best possible solution to preserve the environmental quality in this region.

An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques

This study examines urban growth to support sustainability by investigating urban sprawl in the Vila Velha Urban Agglomeration (UA). If the spatial area of Vila Velha UA increases by 24.36%, from 22.98 km2 in 1994 to 51.64 km2 in 2024, urban sprawl may affect surrounding areas. Landsat satellite images from 1994, 2004, 2014, and 2024 were analyzed to study the spatiotemporal pattern of urban growth. Shannon's entropy index identified urban sprawl in Vila Velha UA. The QGIS software's MOLUSCE (Modules for Land Use Change Simulations) plug-in, utilizing Cellular Automata (CA) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP) models, forecasted urban expansion for 2034. The built-up area is projected to increase from 51.64 km2 in 2024 to 62.31 km2 in 2034. Shannon's entropy index indicated a high rate of urban sprawl. Remote sensing and machine learning techniques are crucial for understanding spatial trends, forecasting future expansion, and informing sustainable urban planning. These methods facilitate an accurate assessment of urbanization dynamics, aiding in developing strategies to manage and mitigate sprawl's adverse impacts. This study offers a unique approach to defining urban growth, emphasizing the need for zoning regulations, green space conservation, and infrastructure development to curb urban sprawl and promote sustainable growth in Vila Velha.

Analysis of application of entropy methods of vibration diagnostic signal processing to assess technical condition of pipelines

The purpose of this article is to review the existing reliability problems of pipeline systems of power complexes. The article considers the existing statistical and logistics systems, which allow to process diagnostic information when assessing the technical condition of pipelines. Modern diagnostic methods are mainly based on the use of vibration, sound, and ultrasonic sensors. The presence of a defect in a pipeline is determined by analysis of the amplitude of a diagnostic signal. Higher efficiency in detecting defects was shown by probability-statistical methods of signal analysis, which are based on chaos theory. One such method is entropy analysis. Analysis of modern signal processing methods has shown that methods based on chaos theory are the most effective. The possibility of using entropy indices as sensitive diagnostic signs is considered. Comparative analysis of signal processing was carried out using entropy methods (Shannon entropy, Kolmogorov entropy) and using known statistical and logistic methods (Fourier Transform, Wavelet Transform, Hilbert-Huang Transform). The analysis results showed that entropy indicators respond to a change in signal structure caused by the presence of a defect in the pipeline or Entropy analysis is a promising method of processing diagnostic signals when assessing the technical condition of pipelines.

COMPETITION LEVEL ANALYSIS FOR THE FINTECH SECTOR IN TURKIYE COMPARED TO GERMANY

Purpose- This study's objective is to observe Türkiye’s fintech market structure by their competitiveness results. To get a better observation Türkiye compared with a fintech-developed country as Germany. The inconvenient definition of fintech caused this study to examine 4 massive fin-tech sectors. Especially current situation of the financial-technology market there are several branches. The study refrains from the engineering-intensive branches to serve its purpose. The study includes; Payments, Lending, Personal Finance, and Insuretech branches. We expect to observe the competitive dynamics for several branches. Methodology- The study selected the “entropy index” as a method. The entropy index shows the market depth for related sectors. It can be defined as a density analysis. We can make the index with at least 5 observations. However, the increase in the observation level causes the low-variance level index results. Findings- The study made its index by the top ten firms by their sales revenue level. The study observes the market in 5 branches. The conclusion part supplied 5 different density results. In that situation, observations can be seen more specifically for the market. The study used the entropy density index to observe the competitiveness level of the market. Results were multiple because the branches were divided from the sector. Every branch has its dynamics. On the Türkiye side, we expect fewer companies than in Germany. However, in the personal finance sector, we can observe the competitiveness levels are close. However, in this study, we can also observe high gaps between the sectors of insurance tech and lending. This study did not have the purpose of determining the factors behind them but the study tried to give political suggestions. Conclusion- The density level for lending and personal finance sectors can be compatible with a high-fintech level company. For the insure-tech side, companies value and numbers are lower. The payment sector has more technology than the other sectors but the number of firms are lower than in Germany. We were expecting the competitive structure are not improve in Turkey because of the low-quantity firms. At some branches, we obtained the results that support this expectation. However, the view of the study observed that some branches has low competitive market structure when compared the Germany. But at some points, there are massive cliffs between the two countries. We can attribute the situation to the habits. The habits of technology meant. Especially on the insurance side, Turkey should improve itself. Türkiye should be getting more investment, subsidies, etc. in the sector of insure-tech and payment sector. Because their competition level is a risk to the country’s market structure. This study includes no detailed politics but suggests to subsidies and incentives from the government side. Government should support and encourage the new-entry firms or entrepreneurs for the sector of fintech.