Indexing Techniques Research Articles

Application of the Weighted Arithmetic Water Quality Index in Assessing Groundwater Quality: A Case Study of the South Gujarat Region

Groundwater is a natural resource used for drinking, agriculture, and industry, apart from surface water. Its quality should be assessed regularly, and the condition of water resources should be maintained accordingly. The most common analytical method for describing and assessing the general water quality is the Water Quality Index (WQI). This study aims to assess the South Gujarat Region’s groundwater quality using the WQI. Various physicochemical parameters like pH, turbidity, total dissolved solids, total hardness, calcium, magnesium, chloride, sulphate, nitrate, fluorides, and total alkalinity are considered for the present study. The data period from 2018 to 2022 is considered for the same. The Weighted Arithmetic Water Quality Index Technique is used to evaluate these data. For checking the potability of the parameters within the acceptable limit, the Indian Standard Drinking Water Specification code (IS: 10050-2012) is adopted. According to the study mentioned above, a few wells’ groundwater quality has been found to be higher than the WQI value. It is also observed that four wells were found unsuitable for drinking purposes in 2018. It is noted that if the WQI value of groundwater is above 51, it is considered harmful to human health; therefore, it requires some kind of processing before use. This study will be beneficial to the policymakers for identifying and providing details about groundwater quality in the form of a specific value, i.e., WQI.

Asymmetric rate of returns and wealth distribution influenced by the introduction of technical analysis into a behavioral agent-based model

Behavioral Finance has become a challenge to the scientific community. Based on the assumption that behavioral aspects of investors may explain some features of the Stock Market, we propose an agent-based model to study quantitatively this relationship. In order to approximate the simulated market to the complexity of real markets, we consider that the investors are connected among them through a Scale-Free network; each one of the investors has his own psychological profile (Imitation, Anti-Imitation, Random); two different strategies for decision making: one of them is based on the trust neighborhood of the investor and the other one considers a technical analysis, the momentum of the market index technique. We analyze the market index fluctuations, the wealth distribution of the investors according to their psychological profiles and the rate of returns distribution. Moreover, we analyze the influence of changing the psychological profile of the hub of the network and report interesting results which show how and when anti-imitation becomes the most profitable strategy for investment. Besides this, an intriguing asymmetry of the rate of returns distribution is explained considering the behavioral aspect of the investors. This asymmetry is quite robust, being observed even when a completely different algorithm to calculate the decision making of the investors was applied to it, a remarkable result which, up to our knowledge, has never been reported before.

A method of gas-related pollution source layout based on multi-source data: A case study of shaanxi province, China

The location and layout of enterprises have an important impact on local air quality. However, a few studies on exploring of the optimal layout of gas-related enterprises from the perspective of optimizing the layout of air pollution sources. This study developed a method for the evaluation of air pollution source layout based on air pollutant emission inventory data, atmospheric self-purification capacity data, and satellite remote sensing air quality data. Taking Shaanxi Province as an example, the Moran's I index and GIS spatial analysis techniques were used to evaluate the layout of air pollution sources, analyze the spatial variation characteristics of air pollution sources, and propose specific countermeasures to optimize the layout of air pollution sources. Results showed that northern Shaanxi and Guanzhong Plain are the most unsuitable for the distribution of NOx and CO sources, accounting for 13.78% and 21.77% of the total area, respectively. The most suitable area for the distribution of NOx is southern Shaanxi, accounting for 65.77% of the total area, mainly concentrated in Hanzhong and Ankang regions. The most suitable area for the distribution of CO is southern Shaanxi, accounting for 40.97% of the total area, mainly concentrated in Hanzhong and Shangluo regions. The findings of this study could supplement and improve the evaluation of the layout of industrial enterprises in China from technical and methodological aspects, and provide new insight for local governments to adjust and optimize the layout of air pollution sources.

Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network

<span lang="EN-US">This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system.</span>

Content-based product image retrieval using squared-hinge loss trained convolutional neural networks

<p><span lang="EN-US">Convolutional neural networks (CNN) have proven to be highly effective in large-scale object detection and image classification, as well as in serving as feature extractors for content-based image retrieval. While CNN models are typically trained with category label supervision and softmax loss for product image retrieval, we propose a different approach for feature extraction using the squared-hinge loss, an alternative multiclass classification loss function. First, transfer learning is performed on a pre-trained model, followed by fine-tuning the model. Then, image features are extracted based on the fine-tuned model and indexed using the nearest-neighbor indexing technique. Experiments are conducted on VGG19, InceptionV3, MobileNetV2, and ResNet18 CNN models. The model training results indicate that training the models with squared-hinge loss reduces the loss values in each epoch and reaches stability in less epoch than softmax loss. Retrieval results show that using features from squared-hinge trained models improves the retrieval accuracy by up to 3.7% compared to features from softmax-trained models. Moreover, the squared-hinge trained MobileNetV2 features outperformed others, while the ResNet18 feature gives the advantage of having the lowest dimensionality with competitive accuracy.</span></p>

Nutrient and heavy metal dynamics in the coastal waters of St. Martin’s island in the Bay of Bengal

Seasonal variation observations were conducted in the coastal waters of St. Martin's Island in the Bay of Bengal to examine the influence of physical processes and the distribution pattern of nutrients in the ocean water. Pollution evaluation indices, health index and statistical techniques were incorporated to assess the heavy metal contamination. Two seasons, cool dry winter and pre-monsoon hot, were considered for sampling from 12 stations around the island. The Cool dry winter season has higher nutrient concentrations than the Pre-monsoon Hot season. The concentration of nutrients appeared as follows: Silicate > Nitrate > Ammonia > Phosphate > Nitrite. PCA and Pearson's Correlation showed that fresh water from nearby rivers, deep water upwelling, and, in some situations, modest anthropogenic sources are crucial. Hence, low DO and phosphate levels during the pre-monsoon hot season indicate there is a planktonic process like photosynthesis prevailing. The island's north-western and south-eastern regions have higher nutrient concentrations, which may be seasonal and due to wind action. Pb, Cu, As, Cr, Cd, and Zn were also considered to comprehend the island's geo-chemical perspectives and ecological and human health risks. The Pre-monsoon Heavy Metal Pollution Index (HPI) and Heavy Metal Evaluation Index (HEI) demonstrated that some places are much higher than the threshold limit, even though no significantly higher value was detected in the cool winter season. The Nemerow Index, the Total Ecological Risk Index (TERI), indicated that heavy metal contamination was severe to moderate and low to moderate. Finally, Pearson's correlation showed the association between physical and chemical characteristics, similar to PCA and Pearson's correlation for nutrients and heavy metals. Thus, this research may help shed light on the state of the seas around St. Martin's Island. This study may also provide explicit insights for the authority to take the necessary measures to preserve marine ecology and the associated terrestrial ecosystem.

Exploring Composite Indexes for Domain Adaptation in Neural Machine Translation

Domain adaptation in neural machine translation (NMT) tasks often involves working with datasets that have a different distribution from the training data. In such scenarios, k-nearest-neighbor machine translation (kNN-MT) has been shown to be effective in retrieving relevant information from large datastores. However, the high-dimensional context vectors of large neural machine translation model result in high computational costs for distance computation and storage. To address this issue, index optimization techniques have been proposed, including the use of inverted file index (IVF) and product vector quantization (PQ), called IVFPQ. In this paper, we explore the recent index techniques for efficient machine translation domain adaptation and combine multiple index structures to improve the efficiency of nearest-neighbor search in domain adaptation datasets for machine translation task. Specifically, we evaluate the effectiveness when combining optimized product quantization (OPQ) and hierarchical navigable small-world (HNSW) indexing with IVFPQ. Our study aims to provide insights into the most suitable composite index methods for efficient nearest-neighbor search in domain adaptation datasets, with a focus on improving both accuracy and speed.

SbMBR Tree—A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining

In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditional lossy compression techniques are hardly suitable due to their inherently random nature. They often impose unpredictable damage to scientific data, which affects the results of data mining and analysis tasks that require certain precision. In this paper, we propose a similarity-based minimum bounding rectangle (SbMBR) tree, a tree-based indexing and compression method, to address the aforementioned problem. Our method can hierarchically select appropriate minimum bounding rectangles according to the given maximum acceptable errors and use the average value contained in each selected MBR to replace the original data to achieve data compression with multi-layer loss control. This paper also provides the corresponding tree construction algorithm and range query processing algorithm for the indexing structure mentioned above. To evaluate the data quality preservation in cross-domain data analysis and mining scenarios, we use mutual information as the estimation metric. Experimental results emphasize the superiority of our method over some of the typical indexing and compression algorithms.

A New Separation Index and Classification Techniques Based on Shannon Entropy

The purpose is to use Shannon entropy measures to develop classification techniques and an index which estimates the separation of the groups in a finite mixture model. These measures can be applied to machine learning techniques such as discriminant analysis, cluster analysis, exploratory data analysis, etc. If we know the number of groups and we have training samples from each group (supervised learning) the index is used to measure the separation of the groups. Here some entropy measures are used to classify new individuals in one of these groups. If we are not sure about the number of groups (unsupervised learning), the index can be used to determine the optimal number of groups from an entropy (information/uncertainty) criterion. It can also be used to determine the best variables in order to separate the groups. In all the cases we assume that we have absolutely continuous random variables and we use the Shannon entropy based on the probability density function. Theoretical, parametric and non-parametric techniques are proposed to get approximations of these entropy measures in practice. An application to gene selection in a colon cancer discrimination study with a lot of variables is provided as well.

Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery

Determining residue cover on agricultural land is an important task. Residue cover helps reduce soil erosion and helps sequester carbon. Many studies have used either spectral indices or classification techniques to map residue cover using satellite imagery. Unfortunately, most of these studies use only a few spectral indices or classification techniques and generally only study an area for a single year with a certain level of success. This manuscript presents an investigation of five spectral indices and six classification techniques over four years to determine if a single spectral index or classification technique performs consistently better than the others. A second objective is to determine whether using the coefficient of determination (R2) from the relationship between residue cover and a spectral index is a reasonable substitute for calculating accuracy. Field visits were conducted for each of the years studied and used to create the correlations with the spectral indices and as ground truth for the classification techniques. It was found that no spectral index/classification technique is consistently better than all the others. Classification techniques tended to be more accurate in 2011 and 2013, while spectral indices tended to be more accurate in 2015 and 2018. The combination of spectral indices/classification techniques outperformed the individual approach. For the second objective, it was found that R2 is not a great indicator of accuracy. Root mean square error (RMSE) is a better indicator of accuracy than R2. However, simply calculating the accuracy would be the best of all.

Attention-based deep learning model to improving multi-criteria decision-making for customer loyalty

Understanding the factors that influence product loyalty is crucial for businesses to effectively attract and retain customers. This study suggests a novel approach to assess the importance and weight of criteria that lead to product loyalty by considering the Halo effect in customer decision-making. The suggested method utilizes an attention-based deep learning model to analyze customer feedback collected through the Net Promoter Score (NPS) scale, incorporating the insights of a large number of customers. The proposed method overcomes the limitations of traditional methods that rely on expert judgments or data mining, providing a more comprehensive and customer-centric perspective. By considering the Halo effect, which can lead to biased perceptions of product features, the method offers a more accurate assessment of criteria weights and their impact on product loyalty. A case study focusing on mobile phone selection and loyalty is conducted to explain the applicability and efficiency of the suggested method. The outcomes are compared with the NPS index and several common multi-criteria decision-making (MCDM) techniques. The findings highlight the superiority of the suggested method in capturing the complex relationships between criteria and product loyalty, surpassing the limitations of expert-based approaches and outperforming traditional MCDM methods. The suggested technique provides valuable insights for companies seeking to enhance customer loyalty and optimize product development strategies. However, it is important to acknowledge limitations related to the reliance on customer feedback and the contextual specificity of the results.

Ki67 proliferation index in medullary thyroid carcinoma: acomparative study of multiple counting methods and validation of image analysis and deep learning platforms.

The International Medullary Thyroid Carcinoma Grading System, introduced in 2022, mandates evaluation of the Ki67 proliferation index to assign a histological grade for medullary thyroid carcinoma. However, manual counting remains a tedious and time-consuming task. We aimed to evaluate the performance of three other counting techniques for the Ki67 index, eyeballing by a trained experienced investigator, a machine learning-based deep learning algorithm (DeepLIIF) and an image analysis software with internal thresholding compared to the gold standard manual counting in a large cohort of 260 primarily resected medullary thyroid carcinoma. The Ki67 proliferation index generated by all three methods correlate near-perfectly with the manual Ki67 index, with kappa values ranging from 0.884 to 0.979 and interclass correlation coefficients ranging from 0.969 to 0.983. Discrepant Ki67 results were only observed in cases with borderline manual Ki67 readings, ranging from 3 to 7%. Medullary thyroid carcinomas with a high Ki67 index (≥ 5%) determined using any of the four methods were associated with significantly decreased disease-specific survival and distant metastasis-free survival. We herein validate a machine learning-based deep-learning platform and an image analysis software with internal thresholding to generate accurate automatic Ki67 proliferation indices in medullary thyroid carcinoma. Manual Ki67 count remains useful when facing a tumour with a borderline Ki67 proliferation index of 3-7%. In daily practice, validation of alternative evaluation methods for the Ki67 index in MTC is required prior to implementation.

Artificial Intelligence Stimulus Response System

The Stimulus Response System (SRS) is based on the concept of artificial intelligence (AI) based mark-up language. (AIML) and is used to train the system for recognizing the user input. By making use of the previously built knowledge base this system is trying to analyze the user input and it provides the response. The Chatter Bot’s, reported in the literature, and was used mainly the indexing technique. The method is strongly dependent on the contents that are stored in a file or a database. Thus there is a need to develop a new system that allows you to carry on conversations with the system as long as possible. Stimulus Response System creates and adds knowledge to a computer. As a result, the computer will appear as human. The main objective of the work is to design an interactive software package which make the computer simple by building content that induces the client to carry on interactive conversations with the system as long as possible.

A Comparison of Mantel-Haenszel and the Area Difference Between Item Characteristic Curves Index Techniques for Detecting Differential Item Functioning

The study is aimed at comparing between Mantel-Haenszel-MH chi-square and the area difference between three item characteristic curves Index -3ICCs by using Raju’s index to detect the differential item functioning DIF in post-graduate general aptitude test according to gender variable. The study sample of (2280) students was distributed equally between male and female groups. The MH techniques revealed a uniform DIF in (43) items, equal to (45%) of the items in post-graduate general aptitude test. 3ICCs technique, revealed a uniform DIF based on gender in two items, equals to (2%) of item's test. MH technique revealed more efficiency in detecting differential item functioning in post-graduate general aptitude test compared to area difference between 3ICCs. The agreement rate was low between these two techniques. Based on these results, the researchers recommended to use more than one technique when detecting the differential item functioning. Keywords: Differential Item Functioning, Mantel-Haenszel, The three-parameter logistic model, post-graduate general aptitude test.

Applying Water Quality Index Technique to Estimate the Euphrates River Suitability for different uses in Samawa and Nasiriya, Southern Iraq

Water salinity assessment is fundamental to the management of water resources. The objective of this research is to calculate the water quality index of the Euphrates River by using mathematical methods. As well as to evaluate the water for various uses by estimating the inorganic pollution in the river water in two sites: Samawa and Nasiriya sites. Based on physiochemical characteristics such as pH, TDS, EC, and concentrations of the major ions of calcium (Ca2+), sodium (Na+), magnesium (Mg2+), potassium (K+), sulphate (SO42-) and Chloride (Cl-), bicarbonate ions (HCO3-), and minor elements of nitrate (NO32-) and total hardness (TH) were applied in this research for the period from the year 2005 to 2021. The results showed that the predominant ions are SO42- and Ca2+ ions in Samawa, while in Nasiriya, the predominant ion are Mg2+ and SO42- ions. According to international and Iraqi standards, the results indicated that water at these two sites along the Euphrates River are unsuitable for drinking. It is low alkalinity and very hard water. Although, the water at these two sites is moderate saline and very good for livestock uses and of doubtful water class, based on Todd’s method of Na % for irrigation water.

The application of logotherapy to improve the meaning of life in emerging adults with self-injury

Self-injury occurs commonly in young people, especially during the development phase of emerging adults. It is seen as a manifestation of existential problems, such as obstacles in fulfilling or pursuing meaning in life which is the most basic human motivation. This study aims to determine the effect of logotherapy as an intervention to increase purpose in life in emerging adults who engage in self-injury. The research utilized a single case experiment A-B design involving two subjects, measuring their level of meaning in life with the Meaning in Life Questionnaire (MLQ). The analysis technique of visual analysis, baseline-corrected tau, and Reliable Change Index (RCI) yielded outcomes indicating logotherapy as an effective intervention option for increasing meaning in life in emerging adults with self-injury.

Evaluation of the Level of Farmland Infrastructure Based on High-Resolution Images of UAV

The evaluation of the level of farmland infrastructure is a necessary objective condition for the use of arable land and agricultural development. In order to investigate the evaluation index system and method of farmland infrastructure level, this article uses the Kenli District of the Yellow River Delta as the research region. In the study region, six typical observation sample areas are chosen. Each area receives high-resolution UAV photos, which are then used to extract information about the farmland infrastructure of the field. A farming infrastructure evaluation index system was built, consisting of 10 indexes for four aspects, including farmland roads, field plots, ditches, and forest belts, using the 100 m by 100 m grid method to divide the evaluation units. The comprehensive index technique was used to calculate the farmland infrastructure score of each unit and identify the degree of excellent, good, and poor farmland infrastructure. The weight of each indication was decided by the hierarchical analysis method. There were 20 excellent grades, 77 good grades, and 29 poor grades among the 126 evaluation units in the study area, with excellent and good grades accounting for 79.13% and area proportions of 14.29%, 64.84%, and 20.87%, respectively. Among the six sample areas, sample areas E and F had the highest percentages of excellent grades, sample area A had 82.62% of the good grades, and all sample areas except A and C had a percentage of poor grades that was higher than 20%. Regularity of the fields, average size of the fields, and the agricultural plot’s slope are the dominant indexes of farmland infrastructure in each observation sample area, and the indexes of the ratio of the perimeter of roads to the perimeter of fields, density of ditches, and the ratio of area of agricultural forest networks to area of fields need to be optimized and improved. The spatial distribution of each grade differs significantly. The evaluation results are consistent with the real situation in the study region and have positive reference meaning for the development and management of farming infrastructure, according to the study’s proposed evaluation system and methodology.

A Novel Hybrid Approach for Prioritizing Investment Initiatives to Achieve Financial Sustainability in Higher Education Institutions Using MEREC-G and RATMI

Financial sustainability is a crucial challenge for higher education institutions due to various challenges and constraints. This necessitates determining their investment priorities accurately based on multiple criteria to ensure a sustainable income. This study puts forward a novel, hybrid approach for prioritizing the financial sustainability plan investment initiatives in higher education institutions using an updated method for the removal effects of criteria with a geometric mean (MEREC-G) and ranking the alternatives based on the trace-to-median index (RATMI) techniques. The developed approach is applied to the strategic financial sustainability plan investment initiatives at King Abdulaziz University (KAU). The study’s results prioritized the investment initiatives based on quantitative and qualitative criteria with different weights. Results also revealed the potential initiatives that can be considered quick-winning initiatives. Furthermore, results determined one high-potential initiative for each of KAU’s financial sustainability plan pillars. Based on the results, the study recommended four steps that assist KAU in ranking the initiatives effectively. Implications of the novel approach include assisting decision makers in higher education institutions to evaluate investment initiative priorities based on objective and subjective criteria to ensure the financial sustainability of their institutions.

The influence of economic development, capital formation, and internet use on environmental degradation in Saudi Arabia

Sustainable economic growth and environmental degradation are two concerns confronting humanity. Saudi Arabia has seen a dramatic rise in its atmospheric carbon emissions during the last three decades. From 1995 to 2020, this research uses the decoupling index and vector error correction model technique to reveal the variables influencing CO2 emissions in Saudi Arabia. This research aims to empirically analyze the causal factors that link carbon emissions, energy use, economic growth, and internet usage. The big priority of this research is to use various techniques and methodologies to examine the cointegration and long- and short-run relationships between macroeconomic variables and stock price. According to the augmented Dickey–Fuller unit root test results, the main series is stationary in the first difference but non-stationary at the level. According to Johansen’s cointegration study, both relations between the variables exist in the Kingdom of Saudi Arabia. The VEC model’s outcome shows that internet use significantly contributes to rising greenhouse gas emissions. When creating strategies for economic growth to achieve sustainable development, policymakers must consider climate change. This may be accomplished by implementing a new policy to switch from conventional to renewable energy sources, emphasizing increased energy efficiency, or reorganizing the energy sector to influence the rise in greenhouse gas emissions.

Microbial Consortium for Polycyclic Aromatic Hydrocarbons Degradation from Petroleum Hydrocarbon Polluted Soils in Rivers State, Nigeria

The study investigated the distribution of polycyclic aromatic hydrocarbon (PAH) degraders across two different petroleum hydrocarbon-polluted sites in the Niger Delta, Nigeria, and the ability of the reconstituted indigenous consortium to utilize these PAHs. Microorganisms were isolated after sample enrichment in naphthalene and anthracene, and biosurfactant production was measured using the emulsification index technique. PAH concentrations of approximately 6000 mg/kg and 9000 mg/kg in Tombia and Bodo were higher than the Department of Petroleum Resources (DPR) intervention limit of 40 mg/kg. The pH, soil texture and high conductivity affected microbial distribution significantly. A total of 12 bacteria from the genera Bacillus, Pseudomonas, Micrococcus and 3 fungal isolates (Fusarium, Aspergillus and Penicillium) from the 2 sites were able to utilize naphthalene and/or anthracene as sole carbon source. While the Tombia site had more microorganisms capable of PAH degradation with the redox indicator 2, 6-dichlorophenol indophenol (DCPIP) (10 bacterial and 3 fungal species), two bacterial species from Bodo were able to produce biosurfactant. The findings of this study indicate that indigenous microbes in the polluted sites are catabolically active and could be further stimulated for an effective eco-friendly and green removal of PAHs from oil-polluted soils while combined.