Construction Of Index Research Articles

Enhancing Leaf Area Index Estimation in Southern Xinjiang Fruit Trees: A Competitive Adaptive Reweighted Sampling-Successive Projections Algorithm and Three-Band Index Approach with Fractional-Order Differentiation

The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, and accurate estimation using hyperspectral technology is essential for monitoring plant health. This study aimed to improve LAI estimation accuracy in apricot, jujube, and walnut trees in Xinjiang, China. Canopy hyperspectral data were processed using fractional-order differentiation (FOD) from 0 to 2.0 orders to extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and their combination (CARS-SPA)—were applied to identify sensitive spectral bands. Various band combinations were used to construct three-band indices (TBIs) for optimal LAI estimation. Random forest (RF) models were developed and validated for LAI prediction. The results showed that (1) the reflectance spectra of jujube and walnut trees were similar, while apricot spectra differed. (2) The correlation between fractional-order differential spectra and LAI was highest at orders 1.4 and 1.7, outperforming integer-order spectra. (3) The CARS-SPA selected a smaller set of feature bands in the 1100~2500 nm, reducing collinearity and improving spectral index construction. (4) The RF model using TBI4 demonstrated high R², low RMSE, and an RPD value > 2, indicating optimal prediction accuracy. This approach holds promise for hyperspectral LAI monitoring in fruit trees.

Text Indexing for Faster Gapped Pattern Matching

We revisit the following version of the Gapped String Indexing problem, where the goal is to preprocess a text T[1..n] to enable efficient reporting of all occ occurrences of a gapped pattern P=P1[α..β]P2 in T. An occurrence of P in T is defined as a pair (i,j) where substrings T[i..i+|P1|) and T[j..j+|P2|) match P1 and P2, respectively, with a gap j−(i+|P1|) lying within the interval [α..β]. This problem has significant applications in computational biology and text mining. A hardness result on this problem suggests that any index with polylogarithmic query time must occupy near quadratic space. In a recent study [STACS 2024], Bille et al. presented a sub-quadratic space index using space O˜(n2−δ/3), where 0≤δ≤1 is a parameter fixed at the time of index construction. Its query time is O˜(|P1|+|P2|+nδ·(1+occ)), which is sub-linear per occurrence when δ<1. We show how to achieve a gap-sensitive query time of O˜(|P1|+|P2|+nδ·(1+occ1−δ)+∑g∈[α..β]occg·gδ) using the same space, where occg denotes the number of occurrences with gap g. This is faster when there are many occurrences with small gaps.

Construction of women's empowerment index for Bangladesh.

This study is dedicated to refining and enhancing the measurement model of women's empowerment in Bangladesh. Women's empowerment, a crucial and multifaceted aspect of societal growth, is often hindered by gender disparities. This is particularly evident in societies like Bangladesh, where women face inequalities in education, economic opportunities, and decision-making power. To address these disparities effectively, it is essential to have a comprehensive understanding of women's empowerment. Therefore, this study aims to refine and enhance the measurement model to capture the multifaceted nature of women's empowerment accurately. To gather data for this study, a structured questionnaire was administered to married women of reproductive age (15-49) in eight Mouza/Mohalla in Dhaka, Bangladesh. This unique approach allowed us to capture a diverse range of perspectives. We used thirty-three indicators across economic, socio-cultural, household, and psychological dimensions to measure women's empowerment. The sample data were then randomly divided for exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify and validate a comprehensive multidimensional framework. Out of 625 respondents, only 36% of women worked, and ~39% married before age 18. Employing thirty-three items in EFA led to identifying eight critical factors (economic independence, control over household financial decisions, household decision-making, reproductive decision-making, freedom of movement, media exposure, positive self-esteem, and negative self-esteem). These factors, which explained 72.661% of the total variance in the data, provide a practical framework for understanding and addressing women's empowerment. Each component was then divided into two sub-dimensions to acquire a better understanding. The CFA indicated a good model fit for each dimension, and convergent and discriminant validity assessments were used to establish reliability and validity, further enhancing the practical implications of our findings. The results of our rigorous exploratory and confirmatory factor analyses not only confirmed the sample structures and internal consistency but also provided significant insights. The findings suggested an adequate fit for all CFA models, indicating the robustness of our measurement model. According to the CFA results, each dimension's variables are satisfactory, and all the dimensions can be combined to create a single index measuring women's empowerment. This comprehensive understanding of women's empowerment, with its specific dimensions and factors, equips policymakers and practitioners with the knowledge to develop a wide range of interventions appropriate to particular facets of empowerment, thereby fostering societal growth and gender equality.

Data‐sparse price indexes by spatio‐temporal regularization and PCA: An application to the Australian housing market

AbstractWe present two novel approaches to overcome the limitations of data‐sparse local house price indexes and combine them into a single model pipeline that is simple, computationally efficient, and interpretable. The first contribution is a new spatio‐temporal regularization of least squares dummy variable models, such as the repeat sales regression used here. This regularization encodes prior knowledge of the proximity of houses in space and their sales in time. It handles missing values in a natural way. The second is nonlocal regularization using truncated principal component analysis (PCA) applied to the resulting national collection of local price indexes. The PCA loadings show that there are important underlying socioeconomic factors that can be leveraged in the construction of Australian market indexes. This PCA reveals important socioeconomic factors, showing that many local markets can be described by a few broad aspects of the national market, consisting of a general trend that contrasts regions influenced by the mining industry with Sydney and Melbourne, and another trend that highlights lifestyle.

Data-driven complementary indices and metrics for assessing national progress on climate risk and adaptation

Climate change is a complex, multidimensional issue requiring decision-making and governance supported by extensive data from social and natural systems. Large cross-country datasets are available, and various methods are used to transform this data into information relevant for policy and decision-making. Summary indices provide insights into adaptation, mitigation, vulnerability, and risk, helping track countries’ climate-related ambitions and progress. However, many existing methods for constructing indices do not fully exploit the multivariate structures within the data, leading to potential redundancies and overlaps. We develop a set of complementary, non-overlapping indices using Principal Component Analysis to capture distinct dimensions of societal and climate interactions. These data-driven indices account for underlying data structures, ensuring each provides unique and independent insights. Our analysis includes harmonized country-level datasets, metrics relevant to loss and damage, public perceptions of climate change, and projections of economic damages. The application of these indices is illustrated with dissonance metrics that assess the alignment between a country’s adaptation capacities, societal concerns, and risks. The proposed approach for index construction can be valuable across various policy contexts and for informing climate-related strategies. An online tool is provided to visualize and access the results presented in this paper.

Construction and Analysis of Chinese Macro-Financial Stability Index

Construction and Analysis of Chinese Macro-Financial Stability Index

Impact of Social Technology Use on Loneliness, Relationship Quality, and Health Among Older Adults During the COVID-19 Pandemic

This study investigated the impact of social technology use on health outcomes through loneliness and relationship quality among older adults during the COVID-19 pandemic. Analyzing data from the 2020 Health and Retirement Study COVID-19 Project, we conducted a structural equation model analysis to assess the effects of social technology use on loneliness, relationship quality, and physical and cognitive health outcomes, with a validated index construct of social technology use. Our findings revealed that more frequent social technology use was significantly associated with younger age, being male, being of White race/ethnicity, and living with a spouse/partner. Higher levels of social technology use and positive relationship quality were related to lower feelings of loneliness, respectively. Loneliness mediated the impact of social technology use on physical and cognitive health outcomes. These results underscore the importance of adopting social technology to enhance the psychological well-being of vulnerable older adults, especially during health crises.

Predictive value and model construction of preoperative nutritional indexes for postoperative leakage in gastric cancer

Objective: We aimed to explore the predictive significance of the nutritional indexes in the occurrence of postoperative leakage after gastrectomy, aiming to develop and validate a predictive nomogram for assessing the risk of these complications.Methods: Patients undergoing radical gastrectomy for gastric cancer were studied, using data from The Sixth Affiliated Hospital of Sun Yat-sen University (2019-2022, n=1075) for nomogram development and an external cohort from Sun Yat-sen University Cancer Center (2022, n=286) for validation. The model, focusing on postoperative leakage, was constructed through univariate and backward stepwise regression. The performance of nomogram was assessed using the receiver operating characteristic (ROC) curve, calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC).Results: The incidence rates of postoperative leakage were 6.51% in the training cohort and 6.71% in the external validation cohort, respectively. The nomogram effectively identifies critical factors influencing postoperative leakage risk, including NRS-2002 score, SFMAI, VSR, blood loss, intraoperative time, type of reconstruction, and Lauren type. The areas under the curve (AUC) for the development and external validation cohorts were 0.763 and 0.761, respectively, demonstrating acceptable predictive accuracy. The validation study showed the nomogram's satisfactory calibration, and both DCA and CIC confirmed its significant clinical utility.Conclusions: The nomogram offers an efficient and precise tool for initial screening, effectively identifying individuals at elevated risk for postoperative leakage.

Investigating water sustainability towards indicators: An empirical illustration using country-level data

This paper contributes to the academic debate on the construction of synthetic indices with respect to water-based sustainability analysis. In this regard, the United Nations Sustainable Development Goal (SDG) 6.4 specifically considers the progress on water-use efficiency within the target of ensuring access to water and sanitation (SDG 6). Our contribution investigates the levels of water sustainability reached by countries around the world considering a suitable measure of the goal obtained by aggregating variables available at the national level, i.e. water efficiency (SDG 6.4.1) and stress (SDG 6.4.2).The construction of a composite index for SDG 6.4 is discussed through the usual steps of normalization, aggregation, and weighting, also highlighting the pros, cons and issues encountered in each step. Fundamental issues faced in the construction of synthetic indices are also discussed: different methods for performing the above steps are compared, and uncertainty analysis is carried out to understand which methods could be reasonably suitable to measure the synthetic indicator of the entire target. As a further theoretical contribution, we illustrate some arguments in favor of the application of categorical scales for normalization, even if the results of some countries seem to be particularly sensitive to the choice of weights in the aggregation phase.Results, obtained after examining univariate and multivariate descriptive statistics, show that the best performances can be observed in small and medium European countries and sub-Saharan African nations (such as Congo, Gabon, and Angola), while the lowest levels appear to be located in Central Asia, the Middle East, and North Africa.A natural implication of this research is its contribution to valorizing the social capital of a regional area, stimulating new approaches to environmental issues, informing public debate, and influencing policy-making.

Assessing Greece’s social vulnerability patterns in times of perma-crisis (2008–2022)

IntroductionThe study aims to map and analyse the development of social vulnerability patterns in Greece through a perma-crisis context (economic crisis, austerity, covid-19, energy, and inflation crisis) over the period 2008-2022.MethodsThe paper rests on the construction of a composite index of four key pillars of social vulnerability (employment, living conditions, health, and education) consisting of 15 selected variables obtained from the official Eurostat datasets and using PCA analysis.ResultsOur findings show that social vulnerability patterns in Greece demonstrate a considerable shift during the stated period where certain social groups (i.e., NEETs and precarious workers) seem to suffer the most. The first pattern (2008-2016) is largely characterized by the intensity and severity of the economic crisis and austerity measures, which contributed to the exacerbation of social vulnerability. The second pattern (2017-2022) presents a gradual decline mainly due to the recovery of the economy and a decrease in unemployment rates featuring a strong tendency to reach the pre-crisis levels in the years to come.DiscussionThe high levels of social vulnerability leading to social exclusion, poverty, and the widening of inequalities, impede the country’s resilience and recovery efforts and undermine social cohesion.

A dynamic authorizable ciphertext image retrieval algorithm based on security neural network inference.

In this paper, we propose a dynamic authorizable ciphertext image retrieval scheme based on secure neural network inference that effectively enhances the security of image retrieval while preserving privacy. To ensure the privacy of the original image and enable feature extraction without decryption operations, we employ a secure neural network for feature extraction during the index construction stage of encrypted images. Additionally, we introduce a dynamic authenticatable ciphertext retrieval algorithm to enhance system flexibility and security by enabling users to quickly and flexibly retrieve authorized images. Experimental results demonstrate that our scheme guarantees data image privacy throughout the entire process from upload to retrieval compared to similar literature schemes. Furthermore, our scheme ensures data availability while maintaining security, allowing users to conveniently perform image retrieval operations. Although overall efficiency may not be optimal according to experimental results, our solution satisfies practical application needs in cloud computing environments by providing an efficient and secure image retrieval solution.

Backbone Index and GNN Models for Skyline Path Query Evaluation over Multi-cost Road Networks

Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on evaluating different SPQ variants, SPQ evaluation is still very inefficient due to the nonexistence of efficient index structures to support such queries. Existing index building approaches for supporting shortest-path query execution, when directly extended to support SPQs, use an unreasonable amount of space and time to build, making them impractical for processing large graphs. In this article, we propose a novel index structure, backbone index , and a corresponding index construction method that condenses an initial MCRN to multiple smaller summarized graphs with different granularity. We present efficient approaches to find approximate solutions to SPQs by utilizing the backbone index structure. Furthermore, considering making good use of historical query and query results, we propose two models, S kyline P ath G raph N eural N etwork (SP-GNN) and T ransfer SP-GNN (TSP-GNN), to support effective SPQ processing. Our extensive experiments on real-world large road networks show that the backbone index can support finding meaningful approximate SPQ solutions efficiently. The backbone index can be constructed in a reasonable time, which dramatically outperforms the construction of other types of indexes for road networks. As far as we know, this is the first compact index structure that can support efficient approximate SPQ evaluation on large MCRNs. The results on the SP-GNN and TSP-GNN models also show that both models can help get approximate SPQ answers efficiently.

The construction and validity assessment of the respiratory air quality health index (AQHI) based on the analytic hierarchy process in Tianjin, China

BackgroundAir quality health index (AQHI), as a developed air quality risk communication tool, has been proved to be more accurate in predicting air quality related health risks than air quality index (AQI) by previous studies. However, the standard method to construct AQHI is summing the excess risks of single-pollutant models directly, which may ignore the joint effect of air pollutant mixtures.MethodsIn this study, a new method which could solve the aforementioned problem, Analytic hierarchy process (AHP), was introduced. Based on this method, we constructed the respiratory health related AQHI using years of life lost (YLL) as indicator of health outcome and compared its validity with AQI.ResultsThere was a correlation between daily AQI and AQHI in 2019 (R2 = 0.830, P < 0.01), and the chi-square test between the two excellent rates showed a statistically significant difference (χ2 = 4.156, P < 0.05). Both AQI and AQHI were correlated with the daily respiratory YLL (P < 0.01), however, the coefficient of AQHI was larger than those of AQI.ConclusionsThis study indicated that compared with AQI, the constructed AQHI based on AHP may predict the health risk of air pollution more effectively. AHP may become a new method to construct AQHI which needs to be proved by taking into consideration by more studies.

Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River-a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

Health assessment method of front landing gear hydraulic retraction and extension system based on GRNN

With the continuous development of civil aircraft health management technology, the status monitoring data of important aircraft systems and components are constantly enriched. The health status of the front landing gear hydraulic retraction system has a great impact on the take-off and landing of the aircraft. Although the system has multi-dimensional monitoring parameters, it is difficult to effectively use the system monitoring data to accurately evaluate its health status. Aiming at the health assessment problem of the front landing gear hydraulic retraction system, this paper builds a simulation model of the system through AMESim software modeling, and studies the influence of the performance changes of hydraulic component parameters on the front landing gear retraction performance. The performance data of the front landing gear retraction actuator at different levels of failure are used as the original data, and the characterization parameters such as retraction time and maximum flow rate are extracted. A health index construction method based on generalized regression neural network (GRNN) is proposed, which can more effectively evaluate the health status of the system. Its effectiveness and accuracy are proved by method comparison.

An approach for constructing spatially paired pseudo repeat-sales housing price indices in China

AbstractConstructing a reliable housing price index is crucial for accurately reflecting housing price dynamics and enhancing transparency in the housing market. However, existing methods for constructing housing price indices often confront many challenges, such as omitted variable issues in the hedonic model and constrained samples used in the repeat-sales model. To address these challenges, this study identifies a spatially paired relationship between complexes and develops a Spatially Paired Pseudo Repeat-Sales model to construct the housing price index. This approach offers two significant advantages: first, it enlarges the sample size used in the repeat-sales model at least 1.8 times; second, it effectively estimates the effects of spatial dependency and physical housing factors on prices while mitigating the impacts of unobservable factors through differentiation. The findings of this paper suggest that using the spatially paired pseudo repeat-sales model can significantly improve the estimation of housing price volatility, by approximately 13%. Moreover, the constructed housing price index model demonstrates significant robustness, even when the spatial weight settings are altered. This research provides convincing evidence of improved housing market transparency for stakeholders, including governments, institutional developers, and individual investors. Additionally, the constructed model can inform other similar research in housing price index construction by providing a spatially pairing perspective on housing complexes.

Innovative development indices for the national economies: on the way to the “ideal” assessment of innovation processes in the countries of the world

Purpose: is to develop recommendations for the construction of new indices of innovative development designed for cross-country comparisons.Methods: the conventional general scientific methods were used in the article, such as the ones of description, comparison, system analysis and historical comparison. The study was based on the documents describing calculation methods of the economic indices and scientific publications covering the history, specifics and problems of the practical application of those indices.Results: it is shown that since the 1930s, when the creation of economic indices drew attention of economic science, indices have evolved from the relatively simple statistical indicators to the complex tools with sophisticated calculation methods. Using several of the most famous or remarkable indices as an example, the principal methods currently used in assessing the level of innovative development are revealed. Basing on the conducted research, the major requirements for composing innovative development indices are stipulated.Conclusions and Relevance: it has been found out that in order to achieve the maximum objectivity in compiling indices of innovative development, the widest possible range of significant indicators should be used and a pool of experts should be engaged in their selection. It is also advisable to engage the pool in compiling the index formula, in particular the indicators weights. Data normalization should be used to ensure comparability of incommensurable indicators, and statistical outliers are advised to be neutralized through standardization techniques. It is also necessary to take into account the goals of the index compiled. At the same time, in the future it is necessary to take into account relationships between the actors of national innovation systems and between different innovation systems, effects of scale and, when possible, sectoral specialization of countries and other national specifics.

Asymmetric-Based Residual Shrinkage Encoder Bearing Health Index Construction and Remaining Life Prediction.

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time-frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.

Price index for computer games: What is hidden from the naked eye?

This paper presents a study of the price dynamics of a wide range of video games in the time period from 2008 to 2020. During the research, a necessary database for analysis was created, video game prices were modeled using econometric methods, and price indices were constructed for various game genres: RPG (Role-Playing Game), strategy (Strategy), action (Action), adventure games (Adventure), causal games (Casual), indie games (Indie), simulators (Simulators) etc. Hypothetically, the price range of video games by genre and system requirements were due to the different amount of effort required to create them. During the construction of hedonistic price indices, prices were cleared from the characteristics of the quality of products (computer games). Video game system parameters were used as characteristics. The work also uses various approaches to modify hedonistic indices by considering genre heterogeneity. Thanks to the study, it is possible to track the dynamics of the behavior of the cost of video games over the considered period of time, with the representation of lagging and advancing trends. Genre diversification of the price elasticity of key variables used during the construction of models was provided.

Development of Energy Security Definition and Indexes for Sustainable Future: A Systematic Literature Review

Abstract In recent years, energy security has been gaining increasing attention worldwide, leading to a rapid growth in research and literature on the subject. As a result, many definitions regarding energy security have been proposed, reflecting the diverse perspectives and considerations involved in ensuring a secure and sustainable energy supply. There has also been a growing interest in how to measure, which has in turn led to the energy security indicators and indices development. Thus, this paper explores the definition of energy security, as well as its various dimensions and the indices employed to measure it. As a result, the scope of energy security has broadened to encompass contemporary concerns such as the environment, governance, social aspects, and technological efficiency, which were not traditionally given much consideration in the past. As a result, multiple themes have been considered, leading to a more holistic understanding and approach to energy security. This evolution has significant implications for the construction of the energy security index especially in Indonesia, as a more comprehensive set of indicators is now required to offer a comprehensive assessment of energy security within a country and provide valuable insights for energy policymakers.