Indexing Techniques Research Articles

Assessing the environmental efficiency of OECD countries through the lens of ecological footprint indices

Environmental efficiency plays a crucial role in achieving sustainable economic development. This study aims to enhance the current understanding of dynamic environmental efficiency by using Data Envelopment Analysis (DEA) in conjunction with the ecological footprint index. This study evaluates 27 OECD countries' environmental performance from 2000 to 2017, employing net capital stock, labor force, and energy consumption as inputs, ecological footprint as undesirable output, and GDP as desirable output. We utilize 16 window Slack-Based Measurement DEA (SBM-DEA) models, each representing consecutive years within the observation period. Additionally, we adopt the Global Malmquist-Luenberger Index (GMLI) techniques to facilitate a simultaneous evaluation of the efficiency levels for each country. Our findings reveal that the United Kingdom and Lithuania were the most and least ecologically efficient countries among the 27 OECD countries, respectively. Over the 18-year observation period, all countries showed both progress and setbacks in environmental efficiency, with a modest overall improvement. Poland, Denmark, Slovakia, and Lithuania were the most improved countries in environmental performance, while Canada and Japan showed the most significant regressions in environmental efficiency. We highlight the need for policymakers to prioritize sustainable economic growth and consider ecological footprints when making economic decisions to enhance environmental efficiency in OECD countries. Our findings have can guide policymakers in designing effective policies and strategies to enhance environmental efficiency and promote sustainable economic development.

An efficient indexing technique for billion-scale nearest neighbor search

An efficient indexing technique for billion-scale nearest neighbor search

Levels and trends in key socioeconomic inequalities in childhood undernutrition in Ethiopia: evidence from Ethiopia demographic and health surveys 2000-2019

IntroductionThe global burden of childhood undernutrition is mainly concentrated in low-income and lower-middle-income countries. In Ethiopia, childhood undernutrition varies significantly across different population groups. The present study is aimed at examining the levels and trends in key socioeconomic inequalities in childhood undernutrition in Ethiopia.MethodsThis cross-sectional study was based on a total pooled sample of 48,782 under-five children drawn from five rounds of the Ethiopia Demographic and Health Surveys (2000–2019). We used multilevel binary logistic regression analysis to determine the key socioeconomic determinants of undernutrition among under five children. In addition, we applied concentration index and decomposition rate analysis techniques to explore the levels and trends in key socioeconomic inequalities in childhood undernutrition.ResultsPlace of residence, household wealth status (approximated by asset-based wealth index), and maternal education, among others, were the key socioeconomic variables significantly associated (p < 0.001) with childhood undernutrition in Ethiopia. The level of inequality was the highest with children from rural areas, poor households and uneducated mothers. Absolute socioeconomic inequality in childhood undernutrition prevalence rate was declined by 9.72 during the study period (2000 to 2019).ConclusionChildhood undernutrition was unequally distributed among under-five children in Ethiopia, and largely concentrated among low socioeconomic status population groups. The identified key socioeconomic drivers of inequality in childhood undernutrition can be used to inform localized interventions and communication strategies to improve nutritional status of under-five children in Ethiopia. The study underscores development of poverty reduction strategies that directly address the existing challenges of the food insecurity and childhood undernutrition for the low-income population groups in the country.

Scalable Identity-Oriented Speech Retrieval

With the prevalence of voice devices in our daily life, speech data is accumulated at an unprecedented speed, forming an invaluable database for security surveillance and financial risk management. In these applications, a key task is given a querying speech snippet to retrieve all speech snippets that are uttered by the same speaker as the querying one, namely Identity-Oriented Speech Retrieval (IO-SR). In this paper, we propose an accuracy and scalable system for IO-SR, which seamlessly integrates speaker modeling and deep indexing techniques. Evaluations on an industrial dataset containing millions of speech snippets show that our system achieves superior performance compared with the state-of-the-art methods.

Implementation of a Noise Filter for Grouping in Bibliographic Databases using Latent Semantic Indexing

Clustering algorithms can assist in scientific research by presenting themes related to some topics from which we can extract information more easily. However, it is common for many of these clusters to have documents that have no relevance to the topic of interest, thereby reducing the quality of the information. We can manage the reduced quality of information of clusters for a bibliographic database by dealing with noise in the semantic space that represents the relations between the grouped documents. In this work, we sustain the hypothesis of using the Latent Semantic Indexing (LSI) technique as an efficient instrument to reduce noise and promote better group quality. Using a database of 90 scientific publications from different areas, we pre-processed the documents by LSI and grouped them using six clustering algorithms. The results were significantly improved compared to our initial results that did not use LSI-based pre-processing. From the perspective of individual performance of the algorithms demonstrating the best results, CMeans was the one that got the highest average gain, with approximately 25%, followed by K-Means and SKmeans, with 17% each; PAM, with 16.5%; and EM, with 15%. The conclusion is that Latent Semantic Indexing has proven to be a helpful tool for noise reduction. We recommend its use to improve the cluster quality of bibliographic databases significantly.

Fast density estimation for density-based clustering methods

Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning. They can deal with non-hyperspherical clusters and are robust to outliers. However, the runtime of density-based algorithms is heavily dominated by neighborhood finding and density estimation which is time-consuming. Meanwhile, the traditional acceleration methods using indexing techniques such as KD-tree may not be effective when the dimension of the data increases. To address these issues, this paper proposes a fast range query algorithm, called Fast Principal Component Analysis Pruning (FPCAP), with the help of the fast principal component analysis technique in conjunction with geometric information provided by the principal attributes of the data. Based on FPCAP, a framework for accelerating density-based clustering algorithms is developed and successfully applied to accelerate the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the BLOCK-DBSCAN algorithm, and improved DBSCAN (called IDBSCAN) and improved BLOCK-DBSCAN (called BLOCK-IDBSCAN) are then obtained, respectively. IDBSCAN and BLOCK-IDBSCAN preserve the advantage of DBSCAN and BLOCK-DBSCAN, respectively, while greatly reducing the computation of redundant distances. Experiments on seven benchmark datasets demonstrate that the proposed algorithm improves the computational efficiency significantly.

Psychometric Properties of the CAGE Questionnaire Amongst Middle-Aged and Older Adults in Khuzistan Province of Iran.

The CAGE questionnaire is an instrument, proved useful in helping to make a diagnosis of alcoholism. The questions focus on Cutting down, Annoyance by criticism, Guilty feeling, and Eye-openers. The questionnaire has been more pervasive simple tool than AUDIT and LAST scales to screen in busy medical settings where limited time is considered for adult patient interviews. The present study aimed to determine the reliability and validity of the CAGE questionnaire in Khuzistan province, Iran and evaluate its role in detecting alcohol-related problems. For this purpose, 382 men with mean age of 65 ± 5 were sampled by cluster-random sampling method in convenience model from the medical centers in eight counties of Khuzistan province during 2019 and they responded to the CAGE questionnaire. The coefficients of Cronbach's alpha (α = 0.82), convergent validity (0.73), divergent validity (-0.06), and criterion validity (0.87) were estimated (ρ < 0.01). The exploratory factor analysis demonstrated that the four -items related to the CAGE for the aged samples are organized into one factor, which clarifies 94% of the variance. Based on the results of the second-order confirmatory factor analysis, all factors were matched up well into a principal factor. Finally, the one -factor model was appropriate for the data by using the fit index techniques for adjusting the scale (AGFI = 0.81, TLI = 0.91, GFI = 0.93, RMSEA = 0.006, IFI = 0.94, NFI = 0.91, CFI = 0.97).The results could prove the well-adjusted reliability and validity of the CAGE and its usefulness for the relevant studies.

Performance Evaluation of Canonical Correlation Analysis and Generalized Canonical Correlation Analysis with Some Continuous Distributed Data

This study was embarked to examine the performance evaluation of canonical correlation and generalized canonical correlation analysis with some continuous distributed data (Gamma, Gaussian, Exponential and Beta). The objectives of the study were to: ascertain if the anthropometric indicators of patients were correlated; ascertain if there is any relationship between vital signs and anthropometric dimensions of patients; obtain the relative efficiency of CCA and GCCA techniques for four continuous distributed simulated data; and determine the model performance adequacy of CCA and GCCA techniques. Real life medical data set was used, consisting of three response variables (Respiration rate, heart rate, temperature) named the vital signs and three predictor variables (Hip circumference, weight, height) named anthropometric dimensions. The study employed the real life data set to simulate data of sample sizes 15, 30, 45, 60, 100, 120, 140, 160, 400, 600, 800 and 1000 for the four continuous distributions. A computer programming language codes were written via R-Studio package to solve the numerous numerical problems in this study. The result of the study revealed that anthropometric dimensions, being the independent variables were not correlated, which implied that there was no symptom of multicollinearity using the Eigen values/condition index technique. In addition, there was significant relationship between vital signs and anthropometric dimensions of patients using Wilks’ Lambda, Hotelling-Lawley Trace, Pillai’s Bartlett Trace and Roy’s Largest Root multivariate statistics. The adequacy of the CCA and GCCA was evaluated using Wilcoxon rank sum test; and the result revealed that GCCA was more efficient than that of CCA for the Gamma and Beta distributed data, while for Gaussian and Exponential distributed data, the relative efficiency of the CCA and GCCA was the same.

Code Search: A Survey of Techniques for Finding Code

The immense amounts of source code provide ample challenges and opportunities during software development. To handle the size of code bases, developers commonly search for code, e.g., when trying to find where a particular feature is implemented or when looking for code examples to reuse. To support developers in finding relevant code, various code search engines have been proposed. This article surveys 30 years of research on code search, giving a comprehensive overview of challenges and techniques that address them. We discuss the kinds of queries that code search engines support, how to preprocess and expand queries, different techniques for indexing and retrieving code, and ways to rank and prune search results. Moreover, we describe empirical studies of code search in practice. Based on the discussion of prior work, we conclude the article with an outline of challenges and opportunities to be addressed in the future.

An Innovative approach towards image encryption by using novel PRNs and S-boxes Modeling techniques

Efficient multiple pseudo-random number sequences (PRNS) and substitution boxes (S-boxes) are one of the most significant building blocks, which are jointly adopted normally for secure data encryption. Multiple aspects pave the way to handle large-scale multimedia data. However, the computational efforts on multiple constructions may certainly lead to limits the required ciphering through-put. Therefore, reducing the computational cost of multiple patterns such as PRNS and S-boxes is the core requirement for an efficient cryptosystem. For this achievement, we exploited the indexing technique over elliptic curves with small prime fields to introduce a computationally efficient mechanism for both multiple PRNS and multiple S-boxes. In the newly constructed PRNS and S-boxes, we used collectively EC group law and simple algebraic operations to get the security strength as well as low computational cost respectively. Based on statistical results, the proposed S-box mechanism is the most effective method that generates efficient multiple S-boxes on minimum prime fields. Likewise, the PRNS’s simulation results shows that the proposed mechanism is the highly effective model for generating multiple verified pseudo random patterns on small prime fields in a single round. These assessments indicates that the proposed mechanism offers desired key-space, better statistical features of encrypted data and less computational cost.

Implementing Video OCR along with SWT Technique for Video indexing and Analysis

Implementing Video OCR along with SWT Technique for Video indexing and Analysis

Prediction of Factors Influencing Social Performance of Indian MFIs using Machine Learning Approach

The aim of the current work is to predict the impact of MFI specific internal factors on the social performance of Indian microfinance institutions (MFIs) by using machine learning techniques. Social performance index (SPI) is designed by taking data of 73 Indian MFIs for 10 years with the help of an indexing technique where six different factors (operational self sufficiency, number of women borrowers, number of rural borrowers, gross loan portfolio, average loan balance per borrower / GNI per capita and cost per borrower) representing different dimensions of functioning of MFIs are considered. The data is taken from MIX data repository. Pooled OLS regression model is used for analyzing impact of various MFI specific factors on SPI. For predicting the SPI, Artificial Neural Networks (ANN) machine learning model is considered that takes all independent variables as input. The results of regression model indicate that size, legal status, outreach and service provisions significantly affect SPI. ANN analysis result indicates that social performance of MFIs gets determined by MFI specific internal factors. The experimental result indicates that the proposed ANN prediction model is providing better result for predicting the SPI. The findings suggest that MFIs can contribute for development of the society by adopting suitable policies keeping in view certain internal factors.

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval.

Recently, content-based image retrieval (CBIR) based on bag-of-visual-words (BoVW) model has been one of the most promising and increasingly active research areas. In this paper, we propose a new CBIR framework based on the visual words fusion of multiple feature descriptors to achieve an improved retrieval performance, where interest points are separately extracted from an image using features from accelerated segment test (FAST) and speeded-up robust features (SURF). The extracted keypoints are then fused together in a single keypoint feature vector and the improved RootSIFT algorithm is applied to describe the region surrounding each keypoint. Afterward, the FeatureWiz algorithm is employed to reduce features and select the best features for the BoVW learning model. To create the codebook, K-means clustering is applied to quantize visual features into a smaller set of visual words. Finally, the feature vectors extracted from the BoVW model are fed into a support vector machines (SVMs) classifier for image retrieval. An inverted index technique based on cosine distance metric is applied to sort the retrieved images to the similarity of the query image. Experiments on three benchmark datasets (Corel-1000, Caltech-10 and Oxford Flower-17) show that the presented CBIR technique can deliver comparable results to other state-of-the-art techniques, by achieving average accuracies of 92.94%, 98.40% and 84.94% on these datasets, respectively.

Parallel processing in data analysis of the JUNO experiment

The JUNO experiment is being built mainly to determine the neutrino mass hierarchy by detecting neutrinos generated in the Yangjiang and Taishan nuclear plants in southern China. The detector will record 5.6 TB raw data every day for offline analysis, but each day it can only collect about 60 neutrino events scattered among huge background events. Selection of extremely sparse neutrino events brings a big challenge to offline data analysis. A typical neutrino physics event normally spans across a number of consecutive readout events, flagged by a fast positron signal followed by a slow neutron signal within a varying-size time window. To facilitate this analysis, a two-step data processing scheme has been proposed. In the first step (called data preparation), the event index data is produced and skimmed, which only contains information of minimum physics quantities of events as well as their addresses in the original reconstructed data file. In the second step (called time correlation analysis), event index data is further selected with stricter criteria. And then, for each selected event, the time correlation analysis is performed by reading all associated events within a pre-defined time window from the original data file according to the selected event’s address and timestamp.This contribution will start to introduce the design of the above data processing scheme and then focus on the multi-threaded implementation of time correlation analysis based on the Intel Threading Building Block (TBB) in the SNiPER framework. Afterwards, this contribution will describe the implementation of distributed analysis using MPI in which the time correlation analysis task is divided into sub-tasks running on multiple computing nodes. At last, this contribution will present the detailed performance measurements made on a multiple-node test bed. By using both skimming and indexing techniques, the total amount of data finally used for neutrino signal time correlation analysis is significantly reduced, and the processing time could be reduced by two orders of magnitude.

Comparative Metabolomic Profiling of Horse Gram (Macrotyloma uniflorum (Lam.) Verdc.) Genotypes for Horse Gram Yellow Mosaic Virus Resistance.

Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an under-utilized legume grown in India. It is a good source of protein, carbohydrates, dietary fiber, minerals, and vitamins. We screened 252 horse gram germplasm accessions for horse gram yellow mosaic virus resistance using the percent disease index and scaling techniques. The percentage values of highly resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible were 0.34, 13.89, 38.89, 46.43, and 0.34, respectively. Repetitive trials confirmed the host-plant resistance levels, and yield loss was assessed. The present disease index ranged from 1.2 to 72.0 and 1.2 to 73.0 during the kharif and rabi seasons of 2018, respectively. The maximum percent yield loss was noticed in the HS (75.0 -89.4), while HR possessed the minimum (1.2-2.0). The methanolic leaf extracts of highly resistant and highly susceptible genotypes with essential controls were subjected to gas chromatography-mass spectrometry analysis. Differential accumulation of metabolites was noticed, and a total of 81 metabolites representing 26 functional groups were identified. Both highly resistant and susceptible genotypes harbored eight unique classes, while ten biomolecules were common. The hierarchical cluster analysis indicated a distinct metabolite profile. Fold change in the common metabolites revealed an enhanced accumulation of sugars, alkanes, and carboxylic acids in the highly resistant genotype. The principal component analysis plots explained 93.7% of the variation. The metabolite profile showed a significant accumulation of three anti-viral (octadecanoic acid, diphenyl sulfone, and 2-Aminooxazole), one insecticidal (9,10-Secocholesta-5,7,10(19)-triene-3,24,25-triol), one antifeedant (cucurbitacin B), and six metabolites with unknown biological function in the highly resistant genotype.

Spatio-Temporal Water Quality Assessment and Pollution Source Apportionment of Lake Chamo using Water Quality Index and Multivariate Statistical Techniques

Despite the current management effort to reduce pollution of water bodies, lakes are far more vulnerable to pollution than the ocean and rivers. Point and non-point pollution are ending on the lake bodies and concerned bodies should trigger interventions. This study aims to assess the effect of pollution on the water quality of Chamo lake using the weighted arithmetic water quality index (WQI) and multivariate statistical methods. Fifteen monitoring points are used to collect water samples from the lake for dry and rainy periods. Twenty-three physical-chemical parameters were analyzed in the water quality laboratory of Arba Minch University, out of which five parameters were analyzed using a multi-meter at the field. The physicochemical water quality analysis result of Lake Chamo revealed that the constituent’s concentration did not show significant variation spatially. The factor analysis result in the Lake Chamo revealed that it had extracted seven principal components that can be explained with a total variance of 86.3%. More than 65% of the monitoring stations in Lake Chamo have an acceptable water quality for irrigation. Moreover, some of the monitoring stations, closer to high human activities, are relatively more polluted than others. Besides, some sensitive parameters such as turbidity have clearly shown that the WQI value can be changed with the parameters changing. Based on clustering analysis, most (47%) of the monitoring are characterized by the recipients of uncontrolled public and state farm and agricultural wastes. The study recommends integrated watershed management and source-based pollution control, which could significantly decrease the pollution level in Lake Chamo after identifying the pollution hotspot areas. Continuous Lake water quality monitoring is necessary for deciding lake pollution.

Water quality index and GIS-based technique for assessment of groundwater quality in Wanaparthy watershed, Telangana, India

A comprehensive study of 58 groundwater samples was collected in the Wanaparthy catchment area (1600 km2), Telangana, India to assess hydrochemistry, quality, water types, and potability using hydrogeochemical characterization, WQI, and GIS techniques. The main concentrations of Na+, K+, Ca2+, Mg2+, Cl−, F−, NO3 −, and SO4 2− ions in groundwater were analysed using ion chromatography (IC). Physicochemical values of potential hydrogen (pH), total dissolved solids (TDS), and electrical conductivity (EC) were determined using portable Hanna meters, while total hardness (TH), alkalinity, and bicarbonates were estimated by titrimetric methods. The obtained results clarify the main anions and cations, which are found in the order Cl− &gt; HCO3 − &gt; SO4 2− &gt; NO3 − &gt; F− and Na+ &gt; Ca+2 &gt; Mg+2 &gt; K+. Among the various ions measured were fluoride (18.97%), chloride (3.44%), nitrate (8.62%), Sulphate (5.17%), sodium (34.48%), and calcium (1.72 %) found above the acceptable limit values of the authority. Indian Standards (BIS) for drinking purposes. According to Piper's trilinear diagram, two dominant hydrochemical facies were identified, Na-Cl-SO4 and Ca-Na-HCO3 types. The Gibbs diagram conferred the dominance of samples in the area, namely the rock-water reaction and the dominance of evaporation. The WQI shows that 67.79% of the samples refer to excellent to good types of water that are suitable for drinking. The drainage diagram calculated that the concentration of the measured parameters exceeds the lower area, which may be caused by the chemical reaction of the rock-water interaction (infiltration and recharge). Since groundwater is the main source of drinking water in the study area, a proper management plan must be put in place before its quality deteriorates.

INDEX OF SOYBEAN TECHNOLOGY ADOPTION AND MULTIVARIATE CORRELATIONS IN SMALLHOLDER SYSTEMS

This study aims to critically highlight appropriate soybean production techniques that enhance levels of agricultural productivity. Therefore, it analyzed soybean production technologies and multivariate correlations in smallholder systems at Jos East, Plateau State, Nigeria. The respondents (120) were selected via multistage techniques. Descriptive statistics, adoption index and multivariate correlation techniques were used to analyze data collected via questionnaires. Soybean production technologies mainly applied by farmers in the area include planting on ridge (81%), improved varieties (80%) and appropriate harvest date (51%); most (67.5%) have a low adoption index of ≤0.36. The correlation coefficients of the multivariate factors (farm output, adoption cost, and adoption index) were significant at rxz (-0.64,), ryz (0.79) and rxy (-0.75), respectively. The identified constraints are barriers to technology adoption. Therefore, the study recommends improved input supply and subsidies, improved advisory services, credit access and market linkages.

New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China.

The prognosis of liver cancer is strongly influenced by microvascular infiltration (MVI). Accurate preoperative MVI prediction can aid clinicians in the selection of suitable treatment options. In this study, we constructed a novel, reliable, and adaptable nomogram for predicting MVI. Using the Surveillance, Epidemiology, and End Results (SEER) database, we extracted the clinical data of 1,063 patients diagnosed with hepatocellular carcinoma (HCC) and divided it into either a training (n = 739) or an internal validation cohort (n = 326). Based on multivariate analysis, the training cohort data were analyzed and a nomogram was generated for MVI prediction. This was further verified using an internal validation cohort and an external validation cohort involving 293 Chinese patients. Furthermore, to evaluate the efficacy, accuracy, and clinical use of the nomogram, we used concordance index (C-index), calibration curve, and decision curve analysis (DCA) techniques. In accordance with the multivariate analysis, tumor size, tumor number, alpha-fetoprotein (AFP), and histological grade were independently associated with MVI. The established model exhibited satisfactory performance in predicting MVI. The C-indices were 0.719, 0.704, and 0.718 in the training, internal validation, and external validation cohorts, respectively. The calibration curves showed an excellent consistency between the predictions and actual observations. Finally, DCA demonstrated that the newly developed nomogram had favorable clinical utility. We established and verified a novel preoperative MVI prediction model in HCC patients. This model can be a beneficial tool for clinicians in selecting an optimal treatment plan for HCC patients.

Wastewater Characterization of Chiniot Drain and Evaluation of Groundwater Contamination Using Water Quality Index (WQI) and GIS Techniques

Groundwater pollution is considered a major concern in today’s world. Most industries in Pakistan produce untreated wastewater, extracting from and contaminating a nearby water source and the soil. This study was conducted in the Nithar Ke area of Chiniot District to assess the impact of the flow of wastewater over the area on groundwater quality. It aimed to: (1) investigate the conditions of industrial wastewater; (2) study the spatial variability of different parameters in groundwater using a GIS map; and (3) check the appropriateness of groundwater for drinking purposes using the water quality index (WQI). Physical and chemical characteristics, including pH, electrical conductivity, total dissolved solids, TSS, CO3, HCO3, chlorides, alkalinity, total hardness, Ca, Mg, arsenic, copper, chromium, nickel, iron, manganese, and sulfate, were tested using groundwater samples, and variance in groundwater was analyzed using a GIS (geographic information system). The water quality index (WQI) was developed for the Nithar Ke area to determine the appropriateness of the groundwater for drinking purposes. WQI values were determined within the range of 41 to 89.5, which showed a poor to good water quality. It was discovered that parameters such as BOD, COD, iron, and TDS had average concentrations of 192 mg/L, 264 mg/L, 2.62 mg/L, and 6039 mg/L, respectively, in the polluted water, which exceeded the permissible limit. Iron and manganese were the most common contaminants found in that area. The biplot and correlation circle showed that iron and turbidity had a positive relationship. Further study on soil stratification and soil characteristics is recommended in this area for a detailed analysis of the causes of metal contamination.