Indicator Values Research Articles

A novel group multi-criteria sorting approach integrating social network analysis for ability assessment of health rumor-refutation accounts

Blooming social media platforms provide breeding ground for health rumors. Despite the establishment of accounts by numerous organizations to counter health rumors, the effectiveness of these endeavors exhibits considerable variability. Thus, there exists a pressing need to refine the framework and operation of rumor-refutation accounts. Aiming at enhancing the proficiency of accounts in refuting health rumors on social media platforms and exploring the factors affecting it, this paper proposes a novel group multi-criteria sorting approach integrating social network analysis (SNA) to classify accounts’ health rumor-refutation ability. To commence, an evaluation indicator system for accounts’ health rumor-refutation ability is established using SNA. Subsequently, the indicator values are computed, incorporating methods such as triangular fuzzy number (TFN), a lite bert (ALBERT) pre-trained language model, and PageRank. Furthermore, hesitant fuzzy linguistic term set (HFLTS) and triangular intuitionistic fuzzy number (TIFN) are used to determine the expert weights and indicator weights. After that, on the basis of original best worst method-sort (BWM-Sort), classification boundaries are discovered creatively using optimal clustering (OC), and minimum discrimination information (MDI) is adopted as the objective function for priority assignment. Consequently, an OC-MDI-BWM-Sort method is newly proposed which offers distinct advantages in computational efficiency, information integration, decision-making objectivity, and result effectiveness. Lastly, regarding to four cases of widely circulated rumors, health rumor-refutation ability of 35 accounts on Weibo platform is classified using the proposed method. The findings underscore that merely 8.57% of accounts exhibit stable and good health rumor-refutation ability, while up to 28.57% and 80.00% display poor and inconsistent ability in certain instances. Tailored to accounts with excellent or good, satisfactory or fair, and poor health rumor-refutation ability, respectively, managerial suggestions are provided regarding information expression standards, account operator proficiency, and account cooperation, and all accounts are advised to watch audience behavior.

Mapping the shared socio-economic pathways onto the Nature Futures Framework at the global scale

AbstractThe Nature Futures Framework (NFF) was developed for the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) to explore scenarios that represent a diversity of positive relationships between humans and nature. Widely used in global environmental assessments, the shared socio-economic pathways (SSPs) in combination with the representative concentration pathways (RCPs) were developed for climate change assessments. However, the relationship at a global level between the SSP–RCP scenario outcomes and the framing of the NFF around three value perspectives—Nature for Nature, Nature for Society, and Nature as Culture—has not been established. Here, we demonstrate a method to map onto the NFF value perspectives results from alternative SSP scenarios, each paired with an RCP consistent with the SSP storyline. For each of the NFF value perspectives, multiple elements were identified, each represented by one or more nature-focused indicators. Values for these indicators, for the different SSP scenario outcomes, were derived from an existing application of a global land system model, LandSyMM. A score for each indicator is estimated by comparing the indicator values against a normative target range. We find that only SSP1 provides greater benefits for Nature as Culture and Nature for Society relative to a 2010 baseline. Overall, the SSP scenarios provide fewer benefits for Nature for Nature, consistent with a bias towards the provision of material over non-material ecosystem services. The results demonstrate that the SSP–RCP scenario framing captures some, but not all, of the dimensions of nature and that alternative scenario framings, such as the NFF, are needed to study a broader range of biodiversity and ecosystem related questions as well as exploring positive futures.

Assessment of the Epidemiological Effectiveness of the Temporary Transfer of Schoolchildren to Distance Learning during the Spread of COVID‑19 by the Example of the Saratov Region

Temporary closure of schools is an effective anti-epidemic measure during periods of rising incidence of acute respiratory viral infections and influenza, which is confirmed by the results of a large number of scientific works. In particular, the effectiveness of this measure consists in reducing the incidence among students. In the context of the spread of COVID‑19 in the Russian Federation, like in many other countries, the temporary transfer of schoolchildren to distance learning has also been repeatedly introduced; however, it remains unclear whether the measure under consideration is effective in relation to the new coronavirus infection.The aim of the study was to assess the epidemiological effectiveness of the temporary transfer of students to distance learning during the spread of COVID‑19 in the Saratov Region.Materials and methods. The analysis was carried out on the basis of statistical data of the Rospotrebnadzor Administration in the Saratov Region on the incidence of COVID‑19 for the period of 2020–2022, as well as materials from published scientific papers.Results and discussion. Analysis of the data on the school closure in 2020 has revealed a decrease in the incidence among schoolchildren (–42.9 %) and an increase in the incidence among people aged 65 years and older (+7.2 %); in 2021, there was a decrease in the incidence among schoolchildren (–15.6 %) and an increase in the indicator value as regards the elderly (+17.8 %); in 2022, another school closure led to an increase in the incidence of COVID‑19 among schoolchildren (+7.8 %) and people over 65 years old (+44.2 %). Based on the results obtained, one can conclude that the measure under consideration is inexpedient due to the fact that the most vulnerable category of the population (persons aged 65 years and older) is actively involved in the epidemic process, which in turn leads to a significant deterioration of the epidemic situation.

Historical sampling error: A neglected factor in long-term biodiversity change research

Long-term time series are increasingly used to assess the effects of global change on plant community diversity and to guide management of target plant communities. However, historical biodiversity data may contain neglected sources of error that can have a significant impact on the results and their interpretation. In our study, we focus on historical sampling error, a source of potential bias in long-term biodiversity assessments that has not been systematically addressed. We resampled two historical datasets of a different origin in the floodplain forests of the Czech Republic, with 534 vegetation plots originally sampled in the 1950s and 1960s. We compared temporal trends in alpha diversity and Ellenberg indicator values (EIVs) between the two parallel surveys. To assess compositional differences, we compared temporal changes in species frequencies. Alpha diversity increased by 9.3 % in one resurvey, but decreased by an average of 30.8 % in the second resurvey. The distribution of EIVs for plots also differed, indicating that each resurvey covered a different part of the environmental gradient. We conclude that preferential historical sampling of the vegetation-environment continuum and species omission may have contributed to the differences in biodiversity and environmental change between the datasets. Our study shows that historical sampling error can have a significant impact on assessments of long-term biodiversity trends. We recommend that historical reference datasets should be critically assessed for potential sources of error in assessments of environmental change and management objectives.

Analisis Cluster Fuzzy C-Means dan Diskriminan untuk Pengelompokan Data Kesejahteraan Rakyat

Cluster analysis is a multivariate method that aims to classify objects into a cluster based on different characteristics. The statistical technique that can be used for clustering is Fuzzy C-means (FCM). The FCM uses membership degree to determine the existence of each data point in a cluster. To ensure accurate clustering and that the criteria are met, cluster validation must be done to produce good data clustering. Moreover, this study uses a discriminant analysis test to validate the results of cluster solutions. The study aims to classify 34 provinces in Indonesia based on the level of people's welfare. The data used in this study is data on indicators of people's welfare in Indonesia in 2021. The variables used in this study were age/life expectancy, the average length of schooling, the 16-18 years of enrollment, and households with proper access to water. Based on the study results, two clusters were formed: cluster 1, areas with low people’s welfare indicator values in 16 provinces, and Cluster 2, regions with high people's welfare indicator values in 18 provinces.

Volatile organic compounds in school environments of Hawassa city, Ethiopia and assessment of possible human health risks

Volatile organic compounds (VOCs) have become a major human wellbeing and environmental concern worldwide. However, evidence on VOC concentrations in primary school environments in Africa, including Ethiopia, is scarce. The goal of this research was to examine VOCs in primary schools in Hawassa, Ethiopia, and to evaluate possible health threats. Concentrations of 76 VOCs were investigated in air samples from classrooms and playgrounds of eight primary schools in Hawassa, Ethiopia. Air samples were taken via passive sampling method using Tenax TA as a sorbent and analyzed by using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Highest total VOCs (TVOCs) concentration (83 μg/m3) was observed in the classroom of School 2 followed by the classroom of School 1 (76 μg/m3), while the smallest TVOC concentration, 37 μg/m3 in the playground of School 8. Among the BTEX, toluene was the most dominant in all samples, ranging from 33% in School 4–38% in School 1 of ∑BTEX. I/O ratios of individual VOC in the schools ranged from 0.44 in School 4 to 9.21 in School 2. The highest cumulative cancer risk (CCR × 106) and the total hazard ratio indicator (THRI) values were 126 and 1.58E-01 respectively, in the classroom of School 4. The CCR and THRI values indicated that the exposure of children to the measured concentrations of benzene may have potentially harmful effects.

Choosing Data Clustering Tools For GIS-Based Visualization Of Disease Incidence In The Population

Objective — To substantiate the choice of optimal tools for clustering spatially referenced data on disease incidence for GIS-based analysis of their spatial distribution. Material and Methods — We used primary data on the incidence of malignant neoplasms, chronic alcoholism, and asthma in the population of eight administrative areas in Arkhangelsk Oblast as a constituent entity of the Arctic Zone of the Russian Federation. Disease incidence was averaged over a 5-year period from 2016 to 2020. We assessed the methods for visualizing the distribution of spatially referenced indicators using the ArcMap geoinformation system tools. Results — The study yielded differences in the outcomes of automated clustering of spatially referenced data in ArcMap, depending on the normality of the distribution in individual samples and the spread of indicator values, which was visually reflected on the resulting map. The parameter values in the samples directly affected the features of data clustering. Hence, this issue is important to consider for ensuring the correct choice of the appropriate analytical tool. Conclusion — Our study demonstrated that when using tools for automated clustering of spatially referenced incidence data in terms of their visualization in ArcGIS, it is necessary to consider the factors that directly affect the accuracy of their presentation. We consider it most appropriate to use a clustering tool based on the geometric interval method.

Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms

Recent developments in machine learning algorithms are playing a significant role in wireless communication and Internet of Things (IoT) systems. Location-based Internet of Things services (LBIoTS) are considered one of the primary applications among those IoT applications. The key information involved in LBIoTS is finding an object’s geographical location. The Global Positioning System (GPS) technique does not perform better in indoor environments due to multipath. Numerous methods have been investigated for indoor localization scenarios. However, the precise location estimation of a moving object in such an application is challenging due to the high signal fluctuations. Therefore, this paper presents machine learning algorithms to estimate the object’s location based on the Received Signal Strength Indicator (RSSI) values collected through Bluetooth low-energy (BLE)-based nodes. In this experiment, we utilize a publicly available RSSI dataset. The RSSI data are collected from different BLE ibeacon nodes installed in a complex indoor environment with labels. Then, the RSSI data are linearized using the weighted least-squares method and filtered using moving average filters. Moreover, machine learning algorithms are used for training and testing the dataset to estimate the precise location of the objects. All the proposed algorithms were tested and evaluated under their different hyperparameters. The tested models provided approximately 85% accuracy for KNN, 84% for SVM and 76% accuracy in FFNN.

Ecological vulnerability assessment of coral islands and reefs in the South China Sea based on remote sensing and reanalysis data

Coral reefs are ecosystems that are highly vulnerable to external environmental impacts, including changes associated with ocean acidification and global warming. Assessing the vulnerability of coral reef growth environments over large areas of the sea is a difficult and complex process, as it is influenced by many variables. There are few studies on environmental vulnerability assessment of coral islands and reefs in the South China Sea. It is therefore particularly important to understand the environmental sensitivity of corals and how coral communities respond to changes in climate-related environmental variables. In this study, indicators were selected mainly from natural environmental factors that hinder the development of coral reefs. The sea surface temperature (SST), sea surface salinity (SSS), wind velocity (WV) and direction, sea level height (SL), ocean currents (OC), and chlorophyll concentration (Chl) of coral reefs in South China Sea Island were integrated to calculate the coral reef environmental vulnerability region. In a GIS environment, Spatial Principal Component Analysis (SPCA) was used to develop sensitivity models and evaluate the ecological vulnerability of coral reefs. Based on the Environmental vulnerability indicator (EVI) values, the study area was classified as 5 grades of ecological vulnerability: Potential (0.000–0.577), Light (0.577–0.780), Medium (0.780–0.886), Heavy (0.886–0.993) and Very Heavy (0.993–1.131). Sensitivity models identified regional gradients of environmental stress and found that some coral reefs in western Malaysia and southwestern Philippines have higher vulnerability. Meanwhile, the study found that the reefs of Paracel Islands and Macclesfield Bank areas of medium vulnerability. Future use of high-precision data from long time series will allow better estimates of site-specific vulnerability and allow for the precise establishment of marine protected areas so that the ecological diversity of coral reefs can be sustained.

Assessment of Herbaceous Plant Composition, Diversity, and Indicator Species in the Juniperus drupacea Forest Openings of the Mountain Parnonas in Greece

A significant challenge in community ecology is the establishment of ecological baselines, which permit the evaluation of the variations in ecological dynamics at different temporal and spatial scales. To our best knowledge, few studies have been conducted in the forest openings of Mt. Parnon to establish a baseline for future monitoring. Hence, a floristic study of the herbaceous plant species composition, diversity, cover, and biomass was conducted in the forest openings of the Mt. Parnon-Natura 2000 Site in Greece to develop an ecological baseline that could be utilized in decision making for conservation and the sustainable use of forest biodiversity and ecosystem services in the forest ecosystem of Mt. Parnon. In the spring season, a thorough floristic survey was performed on Mt. Parnon for two consecutive years, 2021 and 2022. Herbaceous plant composition, diversity, cover, biomass, and plant indicator species (indicator value analysis) in the forest openings of Mt. Parnon were assessed. In the studied area, 63 plant species belonging to 58 genera from 20 families were recorded. The most numerous families were Asteraceae and Poaceae, followed by Fabaceae. Variable plant diversity, herbaceous plant cover, and produced biomass were recorded in different sites. It is noteworthy that some plant species could be regarded as indicators of the sites in the study area [Geranium molle L., Cerastium candidissimum Correns, Vicia villosa Roth, Euphorbia myrsinites L., Odontarrhena muralis (Waldst. & Kit.) Endl., Medicago lupulina L., Lotus corniculatus L., Crepis fraasii Sch. Bip., Bellis sylvestris Cirillo, and Trifolium stellatum L.], and information about these indicators, including Ellenberg type indicator values, is also provided. This study contributes to the understanding of the relevant ecological topics and provides key elements that could be utilized in decision making for the conservation and sustainable use of forest biodiversity and ecosystem services on Mt. Parnon.

An interval water demand prediction method to reduce uncertainty: A case study of Sichuan Province, China

Effective prediction of water demand is a prerequisite for decision makers to achieve reliable management of water supply. Currently, the research on water demand prediction focuses on point prediction method. In this study, we constructed a GA-BP-KDE hybrid interval water demand prediction model by combining non-parametric estimation and point prediction. Multiple metaheuristic algorithms were used to optimize the Back-Propagation Neural Network (BP) and Kernel Extreme Learning Machine (KELM) network structures. The performance of the water demand point prediction models was compared by the Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Kling-Gupta Efficiency (KGE), computation time, and fitness convergence curves. The kernel density estimation method (KDE) and the normal distribution method were used to fit the distribution of errors. The probability density function with the best fitting degree was selected based on the index G. The shortest confidence interval under 95% confidence was calculated according to the asymmetry of the error distribution. We predicted the impact indicator values for 2025 using the exponential smoothing method, and obtained water demand prediction intervals for various water use sectors. The results showed that the GA-BP model was the optimal model as it exhibited the highest computational efficiency, algorithmic stability, and prediction accuracy. The three prediction intervals estimated after adjusting the KDE bandwidth parameter covered most of the sample points in the test set. The prediction intervals of the four water use sectors were evaluated as F values of 1.6845, 1.3294, 1.6237, and 1.3600, which indicates high accuracy and quality of the prediction intervals. The mixed water demand interval prediction based on GA-BP-KDE reduces the uncertainty of the point prediction results and can provide a basis for water resource management by decision makers.

Validating indicators for monitoring availability and geographic distribution of emergency obstetric and newborn care (EmoNC) facilities: A study triangulating health system, facility, and geospatial data.

Availability of emergency obstetric and newborn care (EmONC) is a strong supply side measure of essential health system capacity that is closely and causally linked to maternal mortality reduction and fundamentally to achieving universal health coverage. The World Health Organization's indicator "Availability of EmONC facilities" was prioritized as a core indicator to prevent maternal death. The indicator focuses on whether there are sufficient emergency care facilities to meet the population need, but not all facilities designated as providing EmONC function as such. This study seeks to validate "Availability of EmONC" by comparing the value of the indicator after accounting for key aspects of facility functionality and an alternative measure of geographic distribution. This study takes place in four subnational geographic areas in Argentina, Ghana, and India using a census of all birthing facilities. Performance of EmONC in the 90 days prior to data collection was assessed by examining facility records. Data were collected on facility operating hours, staffing, and availability of essential medications. Population estimates were generated using ArcGIS software using WorldPop to estimate the total population, and the number of women of reproductive age (WRA), pregnancies and births in the study areas. In addition, we estimated the population within two-hours travel time of an EmONC facility by incorporating data on terrain from Open Street Map. Using these data sources, we calculated and compared the value of the indicator after incorporating data on facility performance and functionality while varying the reference population used. Further, we compared its value to the proportion of the population within two-hours travel time of an EmONC facility. Included in our study were 34 birthing facilities in Argentina, 51 in Ghana, and 282 in India. Facility performance of basic EmONC (BEmONC) and comprehensive EmONC (CEmONC) signal functions varied considerably. One facility (4.8%) in Ghana and no facility in India designated as BEmONC had performed all seven BEmONC signal functions. In Argentina, three (8.8%) CEmONC-designated facilities performed all nine CEmONC signal functions, all located in Buenos Aires Region V. Four CEmONC-designated facilities in Ghana (57.1%) and the three CEmONC-designated facilities in India (23.1%) evidenced full CEmONC performance. No sub-national study area in Argentina or India reached the target of 5 BEmONC-level facilities per 20,000 births after incorporating facility functionality yet 100% did in Argentina and 50% did in India when considering only facility designation. Demographic differences also accounted for important variation in the indicator's value. In Ghana, the total population in Tolon within 2 hours travel time of a designated EmONC facility was estimated at 99.6%; however, only 91.1% of women of reproductive age were within 2 hours travel time. Comparing the value of the indicator when calculated using different definitions reveals important inconsistencies, resulting in conflicting information about whether the threshold for sufficient coverage is met. This raises important questions related to the indicator's validity. To provide a valid measure of effective coverage of EmONC, the construct for measurement should extend beyond the most narrow definition of availability and account for functionality and geographic accessibility.

Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case.

As timely information about a project's state is key for management, we developed a data toolchain to support the monitoring of a project's progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the process of setting up the reporting tool just a matter of editing a couple of .json configuration files that specify the names and data format of the project's progress/performance indicators. Since the quantity of data to be provided at each reporting period is potentially overwhelming, some level of automation in the extraction of the indicator values is essential. To this end, it is important to make sure that most, if not all, of the quantities to be reported can be automatically extracted from the experiment data files actually used in the project. The originating use case for the toolchain is a collaborative research project on driving automation. As data representing the project's state, 330+ numerical indicators were identified. According to the project's pre-test experience, the tool is effective in supporting the preparation of periodic progress reports that extensively exploit the actual project data (i.e., obtained from the sensors-real or virtual-deployed for the project). While the presented use case concerns the automotive industry, we have taken care that the design choices (particularly, the definition of the resources exposed by the Application Programming Interfaces, APIs) abstract the requirements, with an aim to guarantee effectiveness in virtually any application context.

Functional states of the hip joint in the early postoperative period with direct anterior arthroplasty with a skin incision «bikini»

BACKGROUND: The prevalence of coxarthrosis at age of 18 years and older has reached 13% in recent years. Total hip arthroplasty is considered the gold standard for the treatment of late-stage coxarthrosis and other hip pathologies. Due to the more frequent intervention performed in recent years and increasing patient expectations, the choice of optimal surgical access will improve the outcomes of the treatment method. However, given the lack of statistical data on the qualitative difference in the postoperative condition of patients using different surgical accesses during hip arthroplasty, there is no consensus on the optimal access choice.
 AIM: To carry out a comparative assessment of the hip joint functional states in the postoperative period after endoprosthesis using direct anterior and anterolateral accesses.
 MATERIALS AND METHODS: The study included 163 patients with coxarthrosis, 71 men and 92 women, who were subsequently divided into 2 groups: the comparison and the main one. To investigate the effectiveness of the proposed approach to endoprosthesis, the results were evaluated by dynamic assessment of joint function using the Barthel scale, the Stand and Walk test, the Leken algo-functional index, the Harris Hip Score scale, and the Health Assessment Questionnaire. The Statistical analysis was performed using StatSoft Statistica 10 and Microsoft Excel 2016 software packages. Intergroup comparisons of quantitative indicators were performed using the Mann-Whitney rank test in case of nonparametric distribution of indicator values and/or significant difference in groups.
 RESULTS: The findings showed that in the postoperative period, the use of direct anterior access in hip arthroplasty was associated with faster recovery of hip joint functionality and shorter inpatient stay compared with anterolateral access, which was confirmed by statistically significant intergroup differences.
 CONCLUSION: The clinical efficacy from using the direct anterior access can be seen in statistically significantly (p 0.05) higher scores on the Barthel self-care scale, the results of the "Stand up and walk" test 26 weeks after surgery, as well as better dynamics of the Harris Hip Score, Leken Index, and Health Assessment Questionnaire 13 months after hip arthroplasty.

Gingerols synergize with anthocyanins to induce antioxidant activity in vitro.

Oxidative stress caused by free radicals contributes to the pathogenesis of multiple chronic health conditions. Phytochemicals protect against oxidative stress; however, low bioavailability from dietary sources limits their health benefits. This study aimed to assess the effects of anthocyanins and gingerols' combination on the cellular antioxidant response of Caco-2 cells against oxidative stress. A strong synergism was observed for anthocyanin-gingerol (Ac-G) w/w combined ratios of 8:1 and 2:1 (dosages of (1 + 0.125) and (1 + 0.5) μg/mL) in the cellular antioxidant activity (CAA) and cytoprotective effects, with synergistic effect indicator (SE) values of 1.41 and 1.61, respectively. The synergism of Ac-G combinations promoted cellular antioxidant defense systems and cytoprotective effects by reducing the induced GPx enzyme activity, protecting SOD enzyme activity, reducing cellular ROS generation, increasing glutathione content, and inhibiting lipid peroxidation. Thus, Ac-G combinations showed potential in supporting the endogenous antioxidant systems to protect cells from oxidation and restore physiological redox status. The Ac-G formulation is a promising healthy option that can be developed into functional foods or nutraceutical products. Furthermore, it could help address the low bioavailability of these phenolics, as higher effects were achieved when combining the same doses.

Influence of Self-Efficacy and Resilience on Subjective Well-Being: A Study of the New Curriculum Implementation in Indonesia

Objective: This research was purposely conducted to determine how teachers’ self-efficacy and resilience influence their subjective well-being, especially in implementing new curriculum policies. Method: This research was a correlational study, while the data were collected from 50 respondents and selected using an incidental sampling technique. The questionnaire was employed to gather the data, and SEM analysis using the SmartPLS program was used to analyze data. Results: Both self-efficacy and resilience had influences on teachers’ subjective well-being. Furthermore, resilience was categorized as high in two of the eight resilience indicators. Meanwhile, one of the five subjective well-being indicators was removed due to the examination of indicator or factor loading values in this study. Novelty: This research differed from others as it primarily addressed the variables used in this research individually and still needed to correlate them as factors that possibly influence each other. Moreover, it provides a new perspective on curriculum development by considering teachers' subjective well-being from a non-financial point of view.

Normalized closeness centrality of urban networks: impact of the location of the catchment area and evaluation based on an idealized network

The decision of where to locate the catchment area of an urban network exerts significant influence on the indicator values and in this research this influence is referred to as the placement effect. Placement effect has significant impact on the studies at the neighborhood scale focusing on the structural properties of the network models, the network analysis results and centrality measures, the inferred movement patterns and the accessibility to destination. Placement effect becomes even more significant when multiple catchment areas are sampled to be compared or classified. This research examines placement effect on one of the most affected indicators, closeness centrality, and proposes using an idealized network as a reference to be compared with the real network in order to find a solution to mitigate the placement effects. By comparing the normalized closeness centrality in the real network with that in the idealized network, we can (1) evaluate the placement effect on the closeness centrality and (2) find the threshold distance in order to mitigate the placement effect. The results show that the closeness centrality of the same node varies remarkably depending on its position and how central it is in the chosen catchment area. Specifically, in the selected areas in this research, if the center point of a catchment area is moved by more than 100 m away from the original center point, the closeness centrality of the same node starts to be significantly influenced by the placement effect. The threshold distance of 100 m offers a recommendation that a direct comparison of the closeness centrality between different nodes in the same catchment area should be drawn only if these nodes are less than 100 m away from each other. In other words, when comparing two nodes located further than the threshold distance from each other, it is advisable to create two separate catchment areas, where these nodes serve as the center points. It should be noted that the threshold distance of 100 m derived specifically from the current research should not be generalized to other cases. The threshold distance of different case studies remains open for further investigation in the future as it may vary among cities or areas.

Context based diversification on keyword search by conceptualization of typed terms of the query

The human era is evolved and dominated through the ultimate intention to know about the Universe and its assets more and more. This in turn persuaded him to gather the immense information of need in the form of theory, tools, intuitions, visuals, and ultimately as the form of abstract objects. In modern Information practice, the art of Conceptualization and the induction of the data driven paradigm identifies the user query intention over a Conceptual network to mold a confident search segment of user choice. The proposed Conceptualization with Typed Terms of Query (CTTQ) approach derives a confident segment of the user query which is contextually measured by means of novel randomized Machine learning algorithms and its surrogates. The Normalized Discounted Cumulative Gain (nDCG) measures of the CTTQ show the usefulness of the query suggestions. It's efficiency improvements are represented with varying impact lexical feature indicator (θ) values.

Data-Driven Prognostics and Diagnostics of Industrial Machinery — A Turbofan Engine Case Study

A machine’s Remaining Useful Life (RUL) is the expected life or usage time remaining before the machine requires repair or replacement. In data-driven methods, typical RUL estimation is performed using models trained with health condition indicator values derived from measured system data. A significant challenge in developing an RUL estimation model is transforming large, multivariate, noisy sensor datasets into useful format(s) that make the data analysis and processing pipeline efficient and extract valuable condition indicators from the data. This work uses the N-CMAPSS dataset to explore options and implications for efficiently organizing and storing large time-series datasets to support prognostics and diagnostics applications. We extend the work to demonstrate a predictive maintenance workflow and solution to (1) detect and classify faults in a turbofan engine and (2) estimate the RUL once we detect performance degradation. Under data engineering, we investigate the impact of various file formats and file types on memory and execution time when dealing with large datasets like N-CMAPSS. We analyze, pre-process, and extract/engineer critical features from the transformed dataset by leveraging our understanding of gas turbines' operation (e.g., Brayton Cycle). We also analyze the performance of various engine submodules for different flight phases (climb, cruise, and descent). This work also explains an approach to down-sample the time series data without losing information relevant to our goals. Using the health condition indicators derived and synthesized in the data engineering stage, we train machine learning models for diagnostics (differentiate between healthy operation and seven different types of faults in the turbofan engine) and prognostics (RUL estimation).

Población en riesgo de pobreza y/o exclusión social. Propuesta metodológica para la estimación del indicador AROPE en los municipios de Andalucía

This work presents a methodological proposal for the continuous estimation of the AROPE indicator (Eurostat) –and its components on income and employment– about people at risk of poverty and/or social exclusion in Andalusian municipalities. The predictive power of this method is assessed by an estimation exercise for this indicator during the period 2016-2018, which shows how the methodology provides accurate estimations of the aggregated indicator values for Andalusia and its evolution over time. The results reveal how aggregated data hide not just high heterogeneity of the estimated municipal values but also significant territorial redistribution of poverty and/or social exclusion during the study period.