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ALLEVIATION OF POVERTY THROUGH PANCHAYAT RAJ INSTITUTIONS: A CRITICAL STUDY OF CHALLENGES AND PROSPECTS IN KARNATAKA, INDIA; [REDUO DA POBREZA ATRAV DE INSTITUIES PANCHAYAT RAJ: UM ESTUDO CRICO DOS DESAFIOS E PERSPECTIVAS EM KARNATAKA, DIA]
Purpose: The purpose of this paper is to: Analyse the role of Panchayat Raj Institutions (PRIs) in alleviating poverty in Karnataka, India. Identify the challenges faced by PRIs in implementing poverty alleviation programs. Explore potential solutions to overcome these challenges and improve program effectiveness. Provide recommendations for strengthening the role of PRIs in poverty alleviation efforts. Theoretical reference: This paper draws on several theoretical frameworks, including: heories of poverty alleviation, focusing on the role of local governance and community participation. Theories of decentralization and the devolution of power to local governments. Theories of social justice and equity, emphasizing the need to address the root causes of poverty. Theories of sustainable development, highlighting the importance of integrating economic, social, and environmental considerations. Method: This research is primarily a doctrinal study, relying on a variety of primary and secondary sources: Primary Sources: Statutory enactments: Constitution of India, 1950, Central Government Schemes implemented by PRIs, The Karnataka Gram Swaraj and Panchayat Raj Act, 1993. Policy documents: National Rural Development Policy, Karnataka State Rural Development Policy, Poverty alleviation scheme guidelines. Secondary Sources: Statistical analysis: Government reports and data sets, Research reports and surveys, Research publications: Peer-reviewed articles and books on poverty alleviation, local governance, and development. Case studies: Examples of successful poverty alleviation programs implemented by PRIs. Results: This research identified several key challenges faced by PRIs in implementing poverty alleviation programs in Karnataka: Corruption: Misuse of funds and resources hinders the effectiveness of programs and prevents benefits from reaching the intended beneficiaries. Caste: Deep-rooted social inequalities limit access to resources and opportunities for marginalized communities. Lack of awareness: Many people remain unaware of available schemes and benefits, leading to underutilization of resources. Limited capacity: PRIs often lack the necessary skills and resources to effectively plan, implement, and monitor programs. Lack of coordination: Poor coordination between different levels of government and stakeholders can lead to delays, duplication of efforts, and inefficient resource allocation. Despite these challenges, the research also identified several promising practices and potential solutions: Transparency and accountability: Initiatives like social audits and public hearings can improve transparency and hold PRI officials accountable for program outcomes. Community participation: Engaging communities in program design and decision-making can ensure programs are relevant and address local needs. Capacity building: Training programs can equip PRI officials with the necessary skills and knowledge to manage programs effectively. Technology and innovation: Utilizing technology can enhance program efficiency, data management, and communication with beneficiaries. Partnerships: Collaborations with NGOs, civil society organizations, and private sector can contribute resources, expertise, and innovation. Conclusion: PRIs play a crucial role in alleviating poverty in India. While they face numerous challenges, there are also promising solutions and opportunities for improvement. By investing in capacity building, promoting transparency, fostering community participation, and embracing technology and innovation, PRIs can be empowered to become more effective agents of poverty alleviation in Karnataka and beyond. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
ALLEVIATING DATA STORAGE CHALLENGE THROUGH VIRTUALIZATION OF BLOCKCHAIN EMBEDDED WITH INTERNET OF THINGS
Internet of things is evolving day by day with connected devices with continuous advancement in the devices but the security of IoT is not assured due to its trusted third party with centralized servers. Blockchain is a peer-to-peer network, where each peer is responsible for their task without centralized server, and no need to trust anyone in the network. Blockchain is integrated with IoT to improve their security, because of its feature of tamper-proof. Few issues are happening while integrating blockchain to IoT. The main issue that has to be resolved for a blockchain is the storage issue. Whenever the blockchain is evolving the storage of the blockchain is also increasing. IoT peers in the network have to store the entire blockchain to perform the verification of data and the IoT nodes are not having the capability to store the entire data. In this paper, we are discussing the storage issue of blockchain while integrating it into IoT. We proposed a navel approach to resolve the issues of storage by the virtualization technique. The result shows that virtualization reduces the storage capacity for the IoT peers as compared with the previously proposed methods. 2022, Engg Journals Publications. All rights reserved. -
All-Optical Plasmonic Neurosensor for Self-Learning Anomaly Detection in Smart IoT Systems
An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817nm with a field enhancement of approximately 28x with gap dimensions of 10nm long. Fabricated devices attained resonance of 823nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 10? and overall shift of the resonance at 51nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Alkali-Activated Materials - A Review for Sustainable Construction
New, sustainable low-Carbon Dioxide (CO2) construction materials must be developed for the global building sector to decrease its environmental impact. During the last several decades, Alkali-activated Materials (AAMs) is a Portland cement-free form, have been intensively researched as a potential alternative for ordinary Portland cement concrete (OPCC), with the objective of lowering CO2 emissions while repurposing a large volume of industrial waste by-products. The suitability of using AAMs made up of industrial waste by-products such as blast furnace slag (BFS), calcined clay (metakaolin), and fly ash (FA) was investigated in this study utilizing a performance-based approach that was unaffected by binder chemistry, history, or environmental effect, Binder paste microstructural assessment and influence on engineering effectiveness, including fresh and hardened characteristics of these materials, In the Viewpoints area, we analyze specific premature phase and long-phase performance of AAMs, as well as Upcoming scientific breakthroughs are also discussed in the Viewpoints section. 2022 American Institute of Physics Inc.. All rights reserved. -
Alkali-activated concrete paver blocks made with recycled asphalt pavement (RAP) aggregates /
Case Studies In Construction Materials, Vol.12, pp.2214-5095, ISSN No: 2214-5095. -
Alkali-activated concrete paver blocks made with recycled asphalt pavement (RAP) aggregates
This study was conducted to evaluate the feasibility of using recycled asphalt pavement (RAP) aggregates in alkali-activated concrete paver blocks. Due to drastic growth in road expansion projects in India, there is tremendous amount of RAP generated by milling and digging of existing bituminous roads. Even though RAP gets recycled in new bituminous roads, there is still large volume of this material that gets downgraded, especially in urban areas. Therefore, there is a need to effectively utilize the unused RAP in paving industry. Alkali-activated paver blocks were synthesized with fly ash (FLA), ground granulated blast furnace slag (GGBS), NaOH sol., Na2SiO3 sol., RAP and natural aggregates. Natural aggregates were substituted with RAP aggregates at replacement rates of 0 %, 25 %, 50 %, and 75 % by weight. The developed paver blocks were tested for water absorption, compressive strength, and abrasion resistance according to IS 15658: 2006 standard. The results of the laboratory study showed that inclusion of RAP aggregates in alkali-activated concrete reduce the compressive strength and abrasion resistance of the paver blocks. Though there is reduction in strength, developed paver blocks classified for use in pedestrian and non-motorized facility. The study also found that the use of RAP aggregates in paver blocks incur economic benefits. A maximum reduction of 25.8 % in production cost was observed for RAP inclusive alkali-activated paver blocks. Furthermore, the proposed method provides environmental benefits by reducing consumption of Portland cement and natural aggregates from quarries, and thus makes paving industry more sustainable and environment friendly. 2019 The Authors -
Alkali-activated bricks made with mining waste iron ore tailings
In India, the enormous growth in the housing sector has put tremendous pressure on construction materials such as bricks. Conventional brick production methods include fired bricks and cement blocks. However, conventional methods significantly contribute to environmental carbon emissions and therefore alternative brick production methods have caught the attention of several researchers. Furthermore, the waste generated in various industries can be a useful resource for the construction industry, and in particular, voluminous waste is generated during the beneficiation stage of iron ore concentrate, which can be integrated into the construction industry to achieve sustainable practice. With this quest in mind, this study proposes the utilization of mining waste iron ore tailing (IOT) in alkali-activated bricks. For this purpose, six different brick compositions were synthesized with fly ash, GGBS, and IOT along with Na2SiO3 sol. The raw materials were characterized using various techniques such as X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscope (SEM), and particle size analysis (PSA). Furthermore, a series of standard tests were conducted on the developed bricks to evaluate their strength and durability properties. The developed bricks have presented a maximum compressive strength of 18.45 MPa and minimum water absorption of 12.6%. Besides, the alkali-activated bricks have shown excellent resistance to brick ageing which was attributed to improvement in the microstructure of bricks due to the filling up of voids with products of the polymeric reaction. Finally, it was interesting to notice that with 8% Na2SiO3 as an alkaline activator and with the combination of fly ash and GGBS more than 50% IOT can be utilized to produce good quality bricks at ambient curing conditions. 2022 The Authors -
ALIGNING INVESTMENTS WITH VALUES: CREATING PORTFOLIOS BASED ON CORPORATE SOCIAL RESPONSIBILITY AND NIM
Purpose: This research discusses the importance of corporate social responsibility (CSR) and its link to a financial performance metric called net interest margin (NIM) in the context of non-banking financial companies (NBFCs). CSR initiatives can lead to long-term sustainability and improved financial performance, attracting investors seeking to align their investments with their values. Need for the Study: The research composes portfolios based on financial companies CSR performance and NIM ratios to help investors understand the difference between CSR and financial performance, making investment decisions based on their portfolio goals and values. Striking a balance between sustainability and the financial performance of financial companies, will help investors find a suitable balance between portfolios for investment purposes. Methodology: The authors used data from 55 financial companies for daily returns from 20142015 to 20212022 and used descriptive statistics to measure the performance of portfolios. Findings: The findings suggest that financial companies in India have improved their CSR scores over time, indicating an increased focus on integrating socially responsible practices into their operations. The data also show that NBFCs are catching up with banks regarding CSR scores, and some NBFC portfolios even outperform banks regarding returns. However, the study also highlights the need for some companies to focus more on CSR and business operations. Practical Implications: The results serve as a benchmark for financial companies to assess their relative CSR performance, highlighting the need for companies to focus on integrating socially responsible practices into their operations and guiding areas where companies can improve. 2024 by Ishfaq Hussain Bhat, Shilpi Gupta and Satinder Singh Published under exclusive licence by Emerald Publishing Limited. -
Aligning Green Finance With Climate Governance: Strategies for Mitigating Global Warming
Green finance plays a pivotal role in aligning financial systems with climate policy objectives to mitigate global warming. This chapter examines strategies that integrate green finance with regulatory frameworks, ensuring that capital flows support climate resilience and sustainability. It explores mechanisms such as green bonds, sustainability-linked loans, carbon pricing, and public-private partnerships to mobilize investments toward low-carbon technologies. Additionally, the chapter highlights the role of financial institutions in promoting climate disclosure and risk assessment while addressing challenges like greenwashing and policy misalignment. Case studies illustrate successful implementations of green finance policies in different jurisdictions, offering insights into best practices and regulatory advancements. By fostering collaboration between governments, financial markets, and international organizations, green finance can accelerate the transition toward a net-zero economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Algorithms for the metric dimension of a simple graph
Let G = (V, E) be a connected, simple graph with n vertices and m edges. Let v1, v2 $$\in$$ V, d(v1, v2) is the number of edges in the shortest path from v1 to v2. A vertex v is said to distinguish two vertices x and y if d(v, x) and d(v, y) are different. D(v) as the set of all vertex pairs which are distinguished by v. A subset of V, S is a metric generator of the graph G if every pair of vertices from V is distinguished by some element of S. Trivially, the whole vertex set V is a metric generator of G. A metric generator with minimum cardinality is called a metric basis of the graph G. The cardinality of metric basis is called the metric dimension of G. In this paper, we develop algorithms to find the metric dimension and a metric basis of a simple graph. These algorithms have the worst-case complexity of O(nm). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Algorithms for better decision-making: a qualitative study exploring the landscape of robo-advisors in India
Purpose: This paper explores the current state of Robo-advisory services in India. This paper further highlights the problems experienced by the service providers in disseminating the innovative business model among the Indians. Design/methodology/approach: The study adopts a qualitative approach to investigate the industry experts by conducting semi-structured interviews. The data collected were transcripted and further analyzed using the content analysis technique. Finally, the authors utilized categorization and coding techniques to frame broad study themes. Findings: The study findings reveal that the three pillars of Robo-advisory are ease and convenience, the time factor and transparency in operations. Robo-advisory services are still at a nascent stage in India. Furthermore, keeping the sentiments of Indians in mind, FinTech companies could combine automated Robo-advisory with a human touch of a wealth manager for optimal advisory services. Research limitations/implications: Since the present study is qualitative, the authors cannot generalize the study results. Future research can focus on empirically proving the constructs of the study using quantitative methods. Practical implications: Robo-advisors have a well-established market in developed nations but are still nascent in developing countries like India. The current focus of service providers and regulatory authorities must be to increase awareness among investors by educating the investors and building trust. Originality/value: The present study is the first to qualitatively synthesize the challenges faced by the FinTech service providers in the Indian market. 2023, Emerald Publishing Limited. -
Algorithms Against Manipulation: Safeguarding Consumer Rights in AI Shopping
The growing use of AI driven shopping agents has transformed digital commerce through enhanced personalization, while simultaneously enabling new forms of consumer manipulation, including dark patterns, algorithmic price discrimination, and subscription traps. This chapter critically examines whether existing regulatory frameworks particularly the GDPR, the EUAI Act, and Indias Digital Personal Data Protection Act, 2023 are adequate to address persuasive algorithmic practices. It argues that prevailing consent based regulatory models are structurally ill-equipped to counter real-time and adaptive AI driven manipulation. Drawing on regulatory gaps and contemporary case studies, the chapter advances the normative proposal of classifying advanced AI shopping agents as information fiduciaries. Such a framework would impose enforceable duties of loyalty and care toward consumers, moving beyond notice-and-consent paradigms and strengthening consumer protection in AI-mediated digital marketplaces. Copyright 2026, IGI Global Scientific Publishing. -
Algorithmic Trading: Financial Markets Using Artificial Intelligence
This research study gives an in - depth view of the recent developments in the fields of Machine Learning (ML) and Reinforced Learning (RL) techniques as they are related to various models for forecasting and systems for financial trading. The practical usage of deep learning models, that incorporates Neural Networks such as Recurrent, Convolutional along with hybrid models integrating genetic algorithms with LSTM networks, for forecasting the stock market patterns as well as bank failures, and fluctuations in exchange rate which is addressed in this study in an in - depth review analysis of the latest literature. In addition to this it also investigates how trading algorithm performance as well as risk management can be enhanced by applying techniques of deep reinforcement learning. This study also demonstrates the enhanced, efficacy, precision and the profitability achieved by using these artificial intelligence methods as compared with conventional economic modelling and detailed technical study models by analysing a number of stock markets and different kinds of assets. 2024 IEEE. -
Algorithmic Trading and Machine Learning: An Empirical Study of Stock Price Prediction in India
This research paper uses historical data from Ambuja Cement to compare nine machine learning algorithms for algorithmic trading in the Indian stock market. The algorithms applied include SVM, Linear Regression, Decision Tree, K-NN, Ridge Regression, Lasso Regression, Bayesian Ridge Regression, Random Forest, Elastic Net Regression, XGBoost, and reinforcement learning. MSE, MAE, and R2 are metrics used to evaluate predictive performance. The findings show that ensemble approaches and regularized regressions outperform simpler models, emphasizing the importance of model complexity and feature selection. Reinforcement Learning has the potential for optimizing tactics through constant adaptation. The study provides valuable insights on enhancing algorithmic trading in emerging Indian markets. 2025 IEEE. -
Algorithmic Strategies for Solving Complex Problems in Financial Cryptography
Cryptography is used in applications where subversion of the communication system could lead to financial loss, which is known as financial cryptography. In contrast to classical encryption, which has mostly been utilized for military and diplomatic purposes throughout recorded history, financial cryptography focuses on privacy and security. The techniques and algorithms required for the security of financial transfers as well as the development of new money types are included in financial cryptography. Financial cryptography includes proof of work and several auction mechanisms. Spam is being restricted by using hashcash. The applications of financial cryptography have been observed to be highly diverse. Financial cryptography is incredibly difficult and calls for knowledge from many different, incompatible, or at the very least, hostile disciplines. The higher risk factor that efforts to build financial cryptography systems will reduce or eliminate crucial strategies that they are trapped among financial application and cryptography, or between accountants and programmers. Digital finance is playing a big role in how financial services are organized globally. Digitalization, data analysis, and increased processing power enable a wide range of new financial services and transactions. The importance of economic development has attracted a lot of attention to this economic development enabled by digital financial technology (Fintech). Cryptography has begun to expand swiftly in the Fintech sector, and both investors and financial bankers are becoming more favorable toward digital assets. The observed market factors are directly related to how people behave when they engage in financial activity. The result analysis in this behavioral strategies of financial cryptography from a specific market analysis is still limited, despite the abundance of research and theories on the underlying motives of peoples behavior in financial frameworks. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Algorithmic Justice: Navigating the Ethical and Legal Landscape of AI-Powered Predictive Policing in Twenty-First Century
With the Fourth Industrial Revolution gaining traction, cyber-physical cognitive systems have thrived, fundamentally reshaping the fabric of the society we inhabit. Artificial intelligence (AI) is one such pioneering system, which thrived as a result of the intricate interplay between machine learning algorithms, deep learning (employs neural networks to facilitate automated learning) and vast repositories of structured, semi-structured or unstructured data called big data. AI was further revolutionized by development of techniques such as generative adversarial networks (GANs), transformers and large language models (LLMs) which initiated a new era of predictive analytics. One such prominent deployment of predictive data analytics has been witnessed in the sphere of law enforcement and crime controlling popularly termed as predictive policing (PP). PP is a convulsion of smart society and smart policing that uses data, algorithms and statistical modelling to foster safe, sustainable and better quality of life by optimizing resources and actions of law enforcement agencies (LEAs) to change the landscape of crime management in the society. AI-powered PP is one such breakthrough in advancement of this goal, but there are certain ethical and legal conundrum such as AI ethics, privacy, algorithmic bias and legal uncertainty that are barrier to the adoption of AI for PP. This research paper discusses the ethical and legal quandaries of AI-powered PP with further comparative analysis of key issues in two prominent democraciesUSA and India. Additionally, the paper also puts forth a way forward to achieve sustainable and harmonious use of AI for PP. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Algorithmic Governance in ESG Ratings: Addressing Bias and Enhancing Transparency in AI- Driven Sustainable Finance
ESG ratings serve as guides for corporate investment and strategic decisions and are appended with numerous biases, inconsistencies, and lack of transparency. The present chapter attempts to examine the ratings of 100 companies from different industries and regions and compare S&P Global with MSCI and Sustainalytics. The chapter researches the effects of size, sector, and regional biases on scores using a mixed- method approach. Findings show that energy- intensive sectors are downgraded because of sustainability efforts; large firms get higher grades due to better disclosure, and performance is also higher for companies in jurisdictions marked by stringent regulations. Inconsistencies in ratings are illustrated further by examples of the specific study cases that deal with BlackRock and Tesla. This chapter brings an AI- enabled framework that uses blockchain and machine learning, as well as real- time information, to boost transparency and standardization in response to these issues. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Algorithmic Crypto Trading using EMA Strategy
Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework. 2025 IEEE. -
Algorithmic and Non-Algorithmic Trading Activity in the BSE Using Limit Order Book of Select Stocks
With the existence of a heterogeneous market compounded by asymmetric information, technology has become one of the major newlineenablers in stock market development. Introduction of algorithms for trading gave a fillip to many stock market participants and allowed them to trade rapidly and profitably. In the present day in Indian stock market, newlinewe have two types of market players; algorithmic traders and nonalgorithmic traders. The algorithmic traders are playing a dominant role in order placement, order modification and order execution while the newlinenon-algorithmic traders still continue to use their intuition. This study aims to understand the trading activity of both the market participants. The study uses the Limit Order Book data from Bombay Stock Exchange. newlineThe LOB data of selected nine stocks is considered for the study whose variables namely Order Added, Order Updated and Order Deleted data along with the Bid Ask Quotes are considered for measurement. Based on newlinethe Limit Orders it is observed that there is a statistically significant difference in the trading behavior of algorithmic and non-algorithmic traders based on stock market session timings and market capitalization. newlineThe market making ability of the algorithmic traders was examined using Order-to trade Ratio and it is observed that large number of orders are not executed indicating that there is no significant Market Making happening. newlineThe algorithmic traders possess an edge over the non-algorithmic traders in Order Modification resulting in dominance in the Stock market. The Mann Kendal Trend test indicates upward and downward trend in newlinevolume adjusted spread indicating that market making is happening especially in the stocks where algorithmic activity is high. This study enables regulatory authorities to monitor stock market activity especially during pre- open session. This study provides sufficient scope for further research on future of algorithmic trading activity and its ramifications on non-algorithmic trading activity in the future. -
