Indexing Techniques Research Articles

Bank market power and its determinants: evidence from listed conventional commercial banks from Indonesia

PurposeOver the past three decades, financial deregulation and various reforms have significantly transformed the competitive environment for banks in Indonesia. These changes have introduced new challenges for banks to retain their market power and ensure their survival. In light of this, the article aims to assess the current levels of market power held by Indonesian banks and explore the factors that influence it.Design/methodology/approachThe paper measured the degree of market power and identified its impacting factors for 22 listed commercial banks using the Adjusted Lerner Index (ALI) and appropriate regression technique over a period of 2011–2023.FindingsThe empirical findings reveal that banks in Indonesia enjoy high market power, and factors such as capitalization, diversification, operational inefficiency, asset quality and GDP growth rate significantly impact banks’ market power. Additionally, the findings contradict the structure-conduct-performance paradigm, which advocates that a concentrated banking system impairs competition.Research limitations/implicationsThe study suggests that regulatory authorities should closely monitor the market power levels and promote strategies to enhance competition within the banking sector. Additionally, banks should prioritize implementing measures to reduce operational costs and improve the quality of assets.Originality/valueThis research represents one of the early attempts to gauge the market power of publicly listed conventional commercial banks in Indonesia by employing the Adjusted Lerner Index. Additionally, it introduces “technology adoption” as a novel variable to the analysis alongside other established variables.

Eco-Friendly Synchronous Spectrofluorimetric Determination of Imipenem, Cilastatin, and Relebactam; Application to Market Formulations and Biological Fluids; Greenness Assessment.

The proposed study introduces a rapid, sensitive, and simple synchronous spectrofluorimetric technique for simultaneous quantification of relebactam, cilastatin, and imipenem in marketed pharmaceutical forms and biological fluids. Using synchronous fluorescence spectroscopy at Δ λ = 110nm, cilastatin was detected at 360nm. Fourier Self-Deconvolution was subsequently applied to the spectrum to estimate relebactam and imipenem at 430nm and 470nm, respectively after detection of cilastatin at 360nm ensuring no cross-interference. The pH was adjusted to 8.0 using 2.0 mL of alkaline borate buffer. This approach allowed for the precise quantification of relebactam, cilastatin, and imipenem through ranges of 50-400 ng mL- 1, 20-500 ng mL- 1, and 50-500 ng mL- 1 respectively. The lower detection and quantitation limits were 9.9 and 29.7 ng mL- 1 for REL, 4.5 and 13.6 ng mL- 1 for CIL and 5.5 and 16.5 ng mL- 1 for IMP. The proposed method was successfully applied for the determination of studied drugs in their pharmaceutical formulations with a high degree of accuracy and without interference from common excipients. This approach allowed for the precise quantification of relebactam, cilastatin, and imipenem through ranges of 50-400 ng mL- 1, 20-500 ng mL- 1, and 50-500 ng mL- 1, respectively. The proposed method was rigorously validated according to ICH guidelines. Furthermore, the method's environmental impact was assessed using Eco-scale and Green Analytical Procedure Index (GAPI) techniques.

Advanced Machine Learning Techniques for Precise hourly Air Quality Index (AQI) Prediction in Azamgarh, India

Advanced Machine Learning Techniques for Precise hourly Air Quality Index (AQI) Prediction in Azamgarh, India

Increasing Yield more than Maintaining Fertile Soils Motivated the Choice of Climate-Smart Soil Technologies among Greater Lira Pigeon Pea Farmers

Pigeon pea farmers in Gerater Lira, Uganda, fight the adverse effects of climate change and deteriorating soil fertility. They are combining different strategies and adopting climate-smart soil technologies (CSS technologies) to maintain fertile soil and increase yields. The study explored how the motive for maintaining fertile soils affected CSS technologies' choice. A sample of 39 farmers participated in laddering interviews. Data was analyzed by the means-end chain (MEC) framework and the centrality index (CI) technique. MEC results indicate that farmers predominantly linked crop diversification, addressed dietary needs, increased yields, and increased incomes. In addition, they paid less attention to maintaining fertile soils. Results of the CI highlight goal priorities by gender subgroups with females aged at least 40 paying more attention to producing food, soil fertility, and improving health, while male farmers of the same age category were inclined to spread production risk. Results further showed that male farmers below 40 years of age tend to produce for markets and benevolent, while their female counterparts tend to maintain soil nutrients. Our overall findings could help in the development of targeted strategies to encourage a wider spread of CSS technology use for climate-smart agriculture. This could enhance agricultural resilience in the face of climate change. We recommend encouraging farmers to apply CSS technologies while considering the long-term effects they might have on soil fertility. we further recommend that farmers intensify residual retention to improve soil fertility without requiring money to purchase inorganic fertilizer.

Stability analysis and voltage improvement in DG-integrated distribution networks using VCPI-based critical buses and lines detection considering uncertain power factor

Voltage stability ensures reliable and efficient power transmission to end-users, making it essential for power system efficiency. Dynamic load changes and rising power requirements might cause voltage instability and strain performance. This paper investigates the voltage stability Indicators of distribution systems to address the voltage stability issue in power systems. The uncertain power factor and other numerous technical parameters that affect the voltage drop or voltage collapse are involved in predicting the system’s voltage stability. First, the analysis uses the voltage collapse proximity index (VCPI) technique, which considers bus voltage, impedance, uncertain power factor of load buses, and transmission line impedance at both ends. The applied technique correctly predicts and detects weak buses and transmission lines using different voltage stability indices since technical parameters are correlated with the VCP index. The index and line impedance correspond; consequently, the recommended indication compensates for voltage loss owing to impedance. Therefore, the VCPI is a better index for anticipating voltage collapse and finding network weak busses and lines. The implemented approach is simple, precise, and economical; the index technique detects weak buses and forecasts system collapse. Second, this work introduces EMFO-FPSO, a hybrid metaheuristic that improves voltage stability by combining Fractional-order particle swarm optimization (PSO) and Entropy design mouth flame optimization (MFO). The proposed approach is validated on the IEEE 33 bus system by identifying weak buses and optimizing distributed generation placement in the distribution system. The implemented solution approach based on VCPI and EMFO-FPSO optimization achieves a significant power loss reduction of 71.5% compared to the base case, whereas the reductions for ABC, ACO-ABC, and PSO are 57.3%, 60.7%, and 64.4%, respectively. Moreover, the proposed method attains a stable VCPI Index of 0.90, indicating a 10% enhancement in voltage stability relative to the base case. The study’s results, key findings, and comparisons show the relevance of the proposed method for voltage stability enhancement.

Level of Consumer Satisfaction Towards Brown Rice in Malang District: A Customer Satisfaction Index (CSI) Analysis

In general, the current demand for brown rice does not receive the same attention from the public as white rice. So, it is important to further understand consumer behavior related to consuming brown rice because this has an impact on consumer satisfaction and has an impact on the strategies formulated by producers. Thus, this study aims to assess the level of consumer satisfaction with brown rice and evaluate the performance and importance of certain attributes. The variables analyzed include product quality, price, and benefits. This study applied a quantitative approach with Accidental Sampling technique, involving 100 respondents who were analyzed using the Customers Satisfaction Index (CSI) technique. The results of these findings indicate that the product quality variable (Taste) has the highest level of importance and performance, with values of 4.56 and 4.49, respectively. The level of satisfaction of brown rice consumers in Pakis District, Malang Regency, reached 74.53%, which is classified as satisfied (range 60.1% - 80.0%). The suggestions given are that producers need to improve the quality of brown rice in terms of taste, color, and texture, while marketers are advised to utilize social media and creative campaigns to attract young consumers and offer discounts for large purchases to increase customer satisfaction.

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Optimized variational mode decomposition for improved bearing fault diagnosis and performance evaluation

This research presents an enhanced methodology for diagnosing bearing faults using Variational Mode Decomposition (VMD) based on L-Kurtosis analysis. The proposed method focuses on selecting optimal parameters for VMD to extract the mode containing the most information related to the fault. The selection of these parameters is based on comparing the energy ratio of each mode and the absolute difference in L-Kurtosis between the Intrinsic Mode Function (IMF) with the highest energy and the original signal. The extracted mode is further refined using a specified kurtosis rate threshold to ensure the most relevant significant modes are captured. The proposed methodology was tested using real fault data from the CWRU, XJTU-SY, and a real-world wind turbine dataset related to electric motors and wind turbine systems. The results demonstrated high accuracy in fault detection compared to other methods such as the Gini Index, correlation, and traditional decomposition techniques like EMD. Furthermore, due to the simple computational nature of the improved VMD method, it is faster and more efficient compared to methods that rely on complex calculations or frequency band analysis, making it suitable for applications requiring real-time, reliable fault diagnosis

Analysis of Growth Trends, Regional Variations and Productivity Metrics of Apple Cultivation in Himachal Pradesh, India

India has seen a spatial escalation on apple growth and it fostered the country’s economy and employment opportunities of the apple growing states. States of Jammu & Kashmir, Himachal Pradesh, Uttarakhand and Arunachal Pradesh are primarily known for apple production. Therefore, we worked on the secondary data on apple growth trends, its production from the different sources and analyzed it through growth rate, decomposition analysis and instability index techniques to quantify the production metrics and trend shift. Our findings reported that between1973–1974 to 2021–2022, there was an increase found in the global area, productivity and production of apples in Asia and India. The output of apples worldwide has found to be escalated at a compound annual growth rate of 2.51%, whereas, India has experienced a notable rise of 3.40 percent annually. The majority of the apple's area, output and productivity came from the Asian region, with growth rates of 3.38, 5.97, and 2.59 %, respectively. Furthermore, Worldwide scenario on apple cultivation reported that Poland had the highest relative instability in apple productivity (21.44%), followed by India (15.96%), China (12.53%), Turkey (11.18%) and USA (8.61%). Area yield effect was used to explain the rise in apple output in Asia, however, area expansion was found as the primary cause of increase in area under apple production. In Himachal Pradesh, Zone III has the greatest average yearly productivity (4.78%), followed by Zone II (3.35 %) and Zone IV (0.57%). In zones III and IV, the expansion of area (96.93%) and yields (85.13%) was the reason for the increase in apple production, however, in zone II, the yield effect (102.05%) was the only cause found for the increase in apple’s production.

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

EASL: Enhanced append-only skip list index for agile block data retrieval on blockchain

The weakness of blockchain is widely recognised as the linear, temporal cost required for retrieving data due to the sequential structure of data blocks. To address this, conventional approaches have relied on database indexing techniques applied to each individual replica copy of a blockchain. However, this only partially addresses the problem, because if the index is not distributed it is not available for devices in the blockchain network. If an index is to be incorporated and distributed within blockchain, the unique attribute of immutability necessitates a more innovative approach. To that end, we propose an Enhanced Append-only Skip List (EASL). This specialised indexing technique utilises binary search with skip lists in blockchain, resulting in a sublinear cost for data retrieval. The EASL indexing technique is maintained by each newly appended blockchain block and offers enhanced readability and robustness using an explicitly recorded index structure. Our proposed technique is 42% more efficient in computing and 60% more efficient in storage consumption than its predecessor, the Deterministic Append-only Skip List (DASL) indexing technique. This is achieved through agile data retrieval, resulting in energy cost savings from less computational effort to maintain the index, and less network bandwidth to retrieve blockchain data. The code for the proposed technique is publicly available on GitHub {https://github.com/jarednewell/EASL/}, to expedite future research and encourage the practical application of this effectual data index.

Assessing sources of groundwater quality and health risks using graphical, multivariate, and index techniques from a part of Rajasthan, India

Groundwater provides sustainable potable water in developing countries like India. However, contaminated groundwater directly affects human health. The United Nation policy is to provide clean water for all by 2030. Therefore, groundwater samples collected from a part of Rajasthan, India were analysed for chemical parameters to identify the sources of groundwater quality variation and associated health risks. To achieve this objective, a comprehensive approach of entropy water quality index (EWQI), Piper and Gibbs diagrams, ionic ratios (IR), synthetic pollution index (SPI), principal component analysis (PCA), and non-carcinogenic health risk (NHR) methods were used. EWQI revealed that groundwater quality was not fit for drinking in 81.86% of the area due to higher TDS, Na+, , , , and contents, which was statistically significant by ANOVA test. Modified Piper’s diagram identified Ca2+ - , Ca2+ - , Na+- , and Na+ - types in 36.67%, 22.73%, 13.64%, and 27.27% of groundwater samples, respectively. Gibbs diagrams illustrated that 59.09% and 40.81% of groundwater samples fell in the rock and evaporation domains, respectively. Ionic ratios demonstrated that geogenic processes (mineral dissolution, ion exchange, and evaporation) and anthropogenic activities cause groundwater quality degradation. PCA indicated that PC1 has high loadings of TDS, Mg2+, Na+, , and , PC2 shows high loadings of pH and , and PC3 represents high loadings of , K+, and , which was further supported by t-test analysis. SPI revealed that 99.82% of the study area is polluted. NHR values (0.03 to 1.68, 0.02 to 1.29, and 0.03 to 1.30 for children, women, and men, respectively) showed that long-term exposure to and -containing drinking water poses a higher health risk to children compared to men and women. Regular water quality monitoring, sanitation facilities, limited application of agrochemicals, etc. were suggested. Such a study would aid environmental public health programs.

Improving Cloud Database Performance Using Storage Technologies

Cloud databases have become indispensable for modern applications due to their scalability, flexibility, and cost-effectiveness. However, optimizing performance in cloud environments can be challenging due to factors such as network latency, data distribution, and resource contention. This paper explores various storage technologies and techniques that can significantly enhance cloud database performance. We delve into the characteristics of different storage types, including solid-state drives (SSDs), hard disk drives (HDDs), and object storage, and discuss their suitability for various workloads. Additionally, we examine caching mechanisms, data compression, and deduplication strategies that can optimize data access and reduce storage costs. Furthermore, we explore the impact of database architecture choices, such as distributed databases and inmemory databases, on performance. We also discuss the importance of effective query optimization and indexing techniques. By understanding and implementing these storage technologies and techniques, organizations can achieve substantial improvements in cloud database performance, leading to better user experiences and increased application efficiency

Class IV composite restoration with direct silicon index technique

Class IV composite restoration with direct silicon index technique

Evaluation of Groundwater Quality for Drinking in Various Districts of Jammu and Kashmir with Special Emphasis on Kashmir

Abstract: In India, groundwater is a valuable resource for humans, yet human activity has consistently reduced its quality. For the management of groundwater resources to be sustainable, an assessment of the quality of the groundwater is required. With the use of integrated-weight water quality index and multivariate statistical techniques, this study sought to assess the groundwater quality in the Indian area of Kashmir for drinking purposes. Eight water quality measures were included in the data, which came from 50 observation wells and included pH, total hardness, nitrate (NO3−), iron (Fe), lead (Pb), mercury (Hg), arsenic (As), and coliforms. The integrated-weight water quality index (IWQI), principal component analysis (PCA), and cluster analysis (CA) were used to examine various districts. The majority of the metrics, according to the results, were within acceptable ranges, however some areas had excessive hardness and Fe contamination. More than 76% of samples had significant coliform contamination found in them. With four clusters of sample locations, the CA results showed that geological locations had a significant influence on groundwater quality. A PCA's three main components may account for 64.8% of the variation in groundwater quality. Groundwater quality was categorized as excellent (74.11%), good (45%), poor (6.4%), very poor (2.9), and undrinkable (9.4) based on IWQI values ranging from 4 to 2761. These classifications were linked to coliform contamination. These results have shed light on the region's groundwater quality state, which is helpful in creating a plan to safeguard the water supply.

Vector Databases

Vector databases represent a significant advancement in data management, tailored to efficiently handle high-dimensional vector embeddings. Unlike traditional databases, which face challenges with the complexity and scale of high-dimensional data, vector databases are engineered for optimal storage, indexing, and retrieval of vector data. This work explores the challenges posed by high-dimensional data, including the curse of dimensionality, and examines how vector databases address these issues through advanced indexing techniques such as Inverted File (IVF), Product Quantization (PQ), and Locality Sensitive Hashing (LSH). It highlights the importance of vector databases in facilitating rapid similarity searches, which are crucial for applications such as recommendation systems and search engines. The discussion also covers the evolution of vector databases and their impact on AI and machine learning.

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

Delineation of potential zones for groundwater recharge using integrated GIS-based AHP and CSI techniques in Ogun Waterside, Southwestern Nigeria

ABSTRACT Ogun Waterside region of southwestern Nigeria experiences seasonal water stress stemming from groundwater overdraft. Therefore, it became imperative to effectively manage the existing groundwater resources to fulfill the anticipated demand for potable water. To achieve this goal, an analysis involving seven spatial parameters: geomorphology, land use/land cover, slope, lineament density, drainage density, soil types, and geology was conducted. These parameters were examined using a combination of geographic information system (GIS)-based analytical hierarchy process (AHP) and cumulative score index (CSI) techniques to delineate potential zones for groundwater recharge. The AHP methodology was employed to assign relative weights to the thematic layers and their corresponding features, reflecting their importance. The AHP results were integrated using a weighted linear combination within a GIS environment to generate the groundwater recharge potential map. Further, the CSI was used to reclassify the composite groundwater recharge potential map. The resultant map revealed three zones which were classified into high, moderate, and low potential zones founded on the CSI approximation. About 98.56 km2 (8.28%) of the study area is categorized as having low potential, 803.77 km2 (67.54%) falls within the moderate potential zone and 287.71 km2 (24.18%) falls in the high potential zone.

Small-scale farmers perception, adaptation choices to climate change and existing barriers: Evidence from northern Ethiopia

Climate change continues to significantly impact agricultural production in Ethiopia that have encountered several environmental problems in recent decades. The study site is one of the areas vulnerable to climate change and influenced by climate variability. This study intends to identify small-scale farmers’ adaptation options, their perception and existing barriers in northern Ethiopia. It is based on an analysis of cross-sectional data collected from 193 randomly selected farm household heads. A multivariate probit model was employed to analyze the factors influencing farmers’ climate change adaptation choices. Constraint Facing Index (CFI) technique was also constructed to identify the existing barriers. The findings indicate that a significant number of farmers (91.2%) reported a trend toward rising temperatures. Similarly, 86.01% of the farmers indicated that the perceived rainfall trend has decreased. Adjusting planting dates (56.5%), integrated soil fertility management practices (47.7%), early maturing crop varieties (53.4%), terracing for soil and water conservation (54.9%), and income diversification (55.4%) were the most common climate change adaptation options practiced by farm households in the study area. Some of these adaptation options have significant and complementary relationships. The farmer’s choice of adaptation option was highly constrained by institutional factors and all these identified factors can be possibly addressed through a better institutional service provision system, for a better farm-level adaptation while considering demographic characteristics as well.

Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.