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Death-worlds, Necropolitics and Decoloniality Colonial Negotiations in Mah
The boundaries of sovereignty are mostly relegated to modern and late modern political thoughts that focus on biopolitical and democratic theories. This paper marks a shift of sovereign subjectivity to the interstitial spaces of life and death of the colonial subjects. Through the study of the necropolitics of colonial control in the erstwhile French colony of Mah as narrated in the novel On the Banks of the Mayyazhi, this paper argues that colonial subjectivity and the idea of sovereignty have decentred itself from the traditional notions of political control and violence to newer avenues of life and death. The perusal of the decolonial approach to necropolitics will examine how colonial logic has shaped the idea of sovereignty. 2024 Economic and Political Weekly. All rights reserved. -
Debrahmanization
This chapter explores the concept of Brahmanism and its pervasive influence on the caste system in India, emphasizing the need for debrahmanization as a means to dismantle caste-based oppression. Historically rooted in the priestly castes dominance, Brahmanism has shaped societal structures by perpetuating inequality, while debrahmanization seeks to challenge this hierarchical order. This chapter examines the theoretical underpinnings of Brahmanism, including its socio-political ideology, and contrasts it with alternative movements, such as Buddhism, which aim to disrupt Brahmanical supremacy. The process of debrahmanization is positioned as a critical act to create an egalitarian society, advocating for political and intellectual engagement with anti-caste movements. Scholars like Ambedkar, Phule, and contemporary thinkers have advanced this discourse, emphasizing the need to address caste discrimination in all areas of life, including education, literature, and politics. Ultimately, this chapter argues that debrahmanization is a necessary step towards achieving social justice and creating a more inclusive and democratic India. 2026 selection and editorial matter, Mahitosh Mandal and Sanjiv Kondekar; individual chapters, the contributors. -
Decent Work Deficit: A Challenge on the Women Empowerment in Indian Agricultural Sector
Women play a crucial role in Indian agriculture, but they also confront several obstacles that reduce their productivity and prevent them from fully engaging in the sectors development. The majority of women in India are employed in agriculture, which is one of the sectors that contributes most to the GDP and is essential to the economic development of the nation. Although women continue to have a significant and recognized role in agriculture, their function is frequently overlooked. Women make up about 75% of the full-time labor force on Indian farms. The nation wont develop unless its women farmers are empowered. Only through decent work labour the agriculture sector will be developed which will help in the empowermentof women agricultural Labourers in India. So the government should take all steps to implement the decent work concept of ILO in the Indian agricultural sector. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Decentralized Data Integrity: Integrating MySQL with Blockchain for Resilient Healthcare Systems
A transformational solution to the problems created by healthcare data management is presented by the integration of MySQL and blockchain technology, centered around security, scalability, and efficiency. This paper presents MBHA MySQL-Blockchain Healthcare Architecture combining thestructured data storage, querying capabilities of MySQL with the decentralized, tamper-proof framework of blockchain. The system shows impressive performance metrics with an average API response time of 1.54 seconds for user registration and 841 milliseconds for login. The database queries and data retrieval or insertion took less than 1 millisecond, and JWT tokens were generated for authentication in less than 50 milliseconds. Conclusion Results indicate an efficient real-time system to accomplish tasks with integrity in terms of data but also with safety in operations. This architectural model, discussed above, is issues regarding data security and access with a need to provide care-collaboration needs. Scalability would then be optimized while keeping down computational overhead; in fact, work toward readiness for adoption is mainly towards being more regulatory compliant. 2025 River Publishers -
Decentralized financial assets: An attraction beyond stock market investments
The Chapter revolves around the attraction that the blockchain-backed decentralized financial products have created in the investment market. These new-gen decentralized finances would be Defi, Metaverse coins, Stablecoins, Cryptocurrency, smart contracts, and privacy coins. The influence of social media and high internet penetration levels have made the retail and partial investors very well aware of many alternate financial investment products. Though there is an increase seen in the Stock Market investments as well a drastic increase in the alternate market is seen. In recent times major stock markets have created indexes separately. It is the icing on the cake with the highest attention of the globe. The regulations have put the Defi Assets under unpredictable volatility but the attraction towards cryptocurrency and other digital asset gain still exists. The study aims to identify the relationship between the increase in investment in the stock market and the alternative. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Deciphering the global research trends and significance of moral intelligence via bibliometric analysis
Introduction: Moral Intelligence (MI) as a concept has gained importance in recent years due to its wide applicability in individual, organizational, and clinical settings or even policy making. The present study employed Bibliometric analysis to understand the emerging topics associated with MI and its global research trend. This papers primary aim was (i) to explore the temporal and geographic growth trends of the research publication on MI. (ii) to identify the most prolific countries, institutions, and authors, working on MI, (iii) to identify the most frequent terminologies, (iv) to explore research topics and to provide insight into potential collaborations and future directions, and (v) to explore the significance of the concept of moral intelligence. Method: Bibliometric analysis was used to understand the emerging topics associated with MI and its global research trend using the SCOPUS database. VOS viewer and R were employed to analyze the result. Through the analysis conducted, the development of the construct over time was analyzed. Results: Results have shown that Iran and the United States and these two combined account for 53.16% of the total country-wise publications. Switzerland has the highest number of Multi-county publications. Authors from Iran and Switzerland have the most number of publications. Emerging topics like decision-making, machine ethics, moral agents, artificial ethics, co-evolution of human and artificial moral agents, green purchase intention etc were identified. Discussion: The application of MI in organisational decision-making, education policy, artificial intelligence and measurement of moral intelligence are important areas of application as per the results. Research interest in MI is projected to increase according to the results delineated in this article. Copyright 2024 Bagchi, Srivastava and Tushir. -
Deciphering the impact of COVID-19 pandemic on food security, agriculture, and livelihoods: A review of the evidence from developing countries
With COVID-19 now spreading in developing countries, massive consequences on health and livelihoods are feared. Food security is the most important and crucial aspect of sustainable development. The agricultural sector forms the backbone of the economy and provides livelihood to a large section in developing countries. Therefore, the disruption in food security and the agricultural sector will have far-reaching impacts on these countries. Owing to the importance of these sectors, this paper performs a comprehensive assessment of the effect of COVID-19 on food security and agriculture. The research suggests coping and mitigation mechanisms that can be adopted to sustain livelihoods. 2020 The Author(s) -
Deciphering the Nature and Dynamics of Gig-Platform Jobs: Workers Hidden Precarity
The technology-driven gig-platform sector has emerged as a new source of employment generation both globally as well domestically. This recent transformation in the labour market is reshaping the nature of labour practices, labour relations, workers rights, and contracts. The sector has huge potential to generate millions of job opportunities by leveraging the use of digital technology. As this sector continues to generate more jobs, such jobs are portrayed as fostering economic growth, while creating meaningful jobs, which are mutually beneficial to workers and employers in terms of providing flexibility and freedom, better earning opportunity, and promoting social inclusion, by which it implies that women are increasingly equipped to find better jobs. This article critically examines the developmental roles of platform jobs which are being particularly highlighted within the policy circle, in academic literature, and tech companies through workers lens. It delves deeper into the discussion on those very aspects of platform jobs just listed, including the flexibility and freedom debate, workers income, and the gender aspect of jobs. In doing so, it carefully examines these aspects with respect to their implications on workers in terms of working conditions and regulatory aspects. The article brings out the workers precarity hidden within those developmental aspects of gig-platform jobs. 2024 CSD. -
Deciphering the non-linear nexus between government size and inflation in MENA countries: an application of dynamic-panel threshold model
Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government sizes estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels. The Author(s) 2024. -
Deciphering the plant growth-promoting traits of bacteria capable of sodium dodecyl sulfate removal from graywater: a sustainable approach for water reuse for irrigation
Sodium dodecyl sulfate (SDS), an anionic detergent found in cleaning products and cosmetics, is one of the chemical pollutants in waterways. SDS-utilizing bacteria were isolated from soil and water samples using 0.05% SDS basal medium. Three bacterial isolates were selected for 16S rRNA sequencing based on their ability to solubilize phosphate, potassium, and zinc, and they were identified as Pseudomonas putida MSK86 OR192890, Klebsiella pneumoniae NET12 OR345422, and Enterobacter sp. MSK86 OR398804. Enterobacter sp. MSK86 and K. pneumoniae NET12 lowered the SDS concentration in the sample 84.78% and 75.65%, respectively, while P. putida MSK86 reduced it 33.43% on the sixth day of incubation. A phosphate-potassium-zinc co-inoculum was prepared using Enterobacter and Pseudomonas species. Laundry wash water was added with the bacteria, individually and co-inoculum, and the fortified water was used to irrigate the Capsicum annuum L. seedlings. On the 45th day, the plants were harvested, and total glucose, protein, chlorophyll, and proline were checked by comparing control plants. Enterobacter sp. MSK86 increased carbohydrate and proline levels by 37.22mg/g ( 0.54 SE) and 2.44mg/g ( 0.1 SE), while K. pneumoniae NET12-treated plants showed an increase in chlorophyll by 1.95mg/g ( 0.02 SE) and total protein by 1.94mg/g ( 0.03 SE). The bacteria in this study showed they could lower SDS levels in graywater and improve farming by adding nutrients to the soil and plants, offering a sustainable way to tackle detergent pollution, fertilizer use, and water scarcity. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Deciphering the properties of UV upturn galaxies in the Virgo cluster
The UV upturn refers to the increase in UV flux at wavelengths shorter than 3000 observed in quiescent early-type galaxies (ETGs), which still remains a puzzle. In this study, we aim to identify ETGs showing the UV upturn phenomenon within the Virgo galaxy cluster. We utilized a colourcolour diagram to identify all potential possible UV upturn galaxies. The spectral energy distributions (SED) of these galaxies were then analysed using the CIGALE software; we confirmed the presence of UV upturn in galaxies within the Virgo cluster. We found that the SED fitting method is the best tool to visualize and confirm the UV upturn phenomenon in ETGs. Our findings reveal that the population distributions regarding stellar mass and star formation rate properties are similar between UV upturn and red sequence galaxies. We suggest that the UV contribution originates from old stellar populations and can be modelled effectively without a burst model. Moreover, by estimating the temperature of the stellar population responsible for the UV emission, we determined it to be 13 000 K to 18 000 K. These temperature estimates support the notion that the UV upturn likely arises from the contribution of low mass evolved stellar populations (extreme horizontal branch stars). Furthermore, the Mg2 index, a metallicity indicator, in the confirmed upturn galaxies shows higher strength and follows a similar trend to previous studies. This study sheds light on the nature of UV upturn galaxies within the Virgo cluster and provides evidence that low-mass evolved stellar populations are the possible mechanisms driving the UV upturn phenomenon. 2024 The Author(s). -
Decision Flow Tracing and Word Impact Analysis in Hybrid Transformer-Conditioned Diffusion Models for Text-to-Image Generation
Text-to-image diffusion models have become a cornerstone of modern generative AI, offering high-quality synthesis yet remaining constrained by their black-box nature, which limits controllability and interpretability. In this work, we propose a hybrid transformer-conditioned diffusion model that integrates UNet-based denoising with multi-head cross-attention transformer blocks at critical latent stages of the diffusion process. The architecture is trained on a curated set of 50,000 samples from DiffusionDB with a 200-step latent diffusion schedule. Text prompts are encoded using a 16-token BERT encoder and mapped into a 256-dimensional latent feature space. Cross-attention layers with eight heads are interlaced within the UNet bottleneck and decoder, enabling token-to-region correspondence and fine-grained semantic propagation. To ensure interpretability, we design an explainability framework that combines hierarchical token-level attention heat maps, temporal attention rollouts, and perceptual ablation studies based on learned image patch similarity. Analysis reveals that object tokens remain spatially and temporally consistent, while attribute tokens demonstrate sharper temporal volatility. JensenShannon divergence quantifies this redistribution of attention across diffusion steps. Experimental evaluation against a standard UNet diffusion baseline demonstrates clear improvements: Frhet Inception Distance decreases by 19.6, CLIP alignment score increases by 5.4, and Inception Score improves by 18.6. Moreover, attention coherence improves by 22%, underscoring the gains in explainability. The proposed framework establishes a pathway toward accountable, high-fidelity, and interpretable text-to-image synthesis. Beyond performance, it supports critical tasks such as bias evaluation, fairness auditing, and quality assurance, offering a robust foundation for the next generation of explainable generative AI systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Decision making framework for foreign direct investment: Analytic hierarchy process and weighted aggregated sum product assessment integrated approach
Foreign direct investment (FDI) plays a paramount role in economic and social growth of every country. FDI acts as a source of external capital and helps in economic growth of the host country. Making decision for FDI during uncertain business environment is a challenge for all stakeholders. Therefore, in this study, we are proposing a decision making framework for FDI. Through literature review, we have identified the factors, on which FDI depends. A process-based, multi-criterion, integrated hierarchical approach for deciding about FDI, has been illustrated. In this study, five sectors are considered, that is, petroleum and natural resource, retailing and e-commerce, healthcare, information technology, and road and highways for illustrating the proposed framework. It is observed that information technology sector has got top priority for FDI followed by retailing and e-commerce and health care sector. Findings will help in taking appropriate decision by stakeholders for FDI. Ultimately it will also help in creating employment, economic growth, and welfare of society at large in the host country. 2021 John Wiley & Sons, Ltd. -
Decision Tree Based Routing Protocol (DTRP) for Reliable Path in MANET
In mobile ad hoc network due to node movements, there exists route failure in active data transmission which results in data loss and communication overheads. Hence, in such a dynamic network, routing through reliable path is one of the tedious tasks. In this paper, we propose a novel Decision Tree based Routing Protocol (DTRP) a data mining technique in route selection process from source to destination. The proposed DTRP protocol selects the one hop neighbors based on the parameters such as speed, Link Expiration Time, trip_time and node life time. Thus the performance of a route discovery mechanism is enhanced by selecting the stable one-hop neighbors along the path to reach the destination. The simulated results show that the lifetime of the route is increased and hence the data loss and end to end delay are minimized thereby increasing the throughput of the network using the proposed DTRP routing protocol compared to existing routing protocols. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Decision-Making Frameworks for Integrating Motion Control in Business Operations
This chapter includes some strategic plans of how to incorporate motions control technologies in the business to promote efficiency, agility, and to make data- driven decisions. It cogitates about the real- time motion tracking and the focus on optimization in the logistics sphere, production and asset management through automation and smart analytics. The chapter explains how AI, IoT, control algorithms help harmonize the processes of motion with the goals of organizations. They lay emphasis on decision theory, performance measures and on the flexibility of motion systems in dynamic environments. The chapter has shed light on the ability of motion control to become the engine of innovation and competition in the operation of the contemporary enterprise by offering scalable and practical integration models. 2026, IGI Global Scientific Publishing. All rights reserved. -
Decision-Making Models for Efficient Outbreak Response: A Management-Orientated Approach to Dengue Control in Andhra Pradesh, India
Dengue remains a serious health challenge across India, and Andhra Pradesh faces repeated outbreaks that put a heavy strain on hospitals, clinics, and communities. Combating this disease isnt just about tracking casesits about making quick, smart decisions to control its spread effectively. This study looks into different decision-making approaches that can help improve how Andhra Pradesh responds to dengue outbreaks, making actions faster and more targeted. Using a mix of existing epidemiological data, interviews with health officials and community leaders, and simulated scenarios, the research explores how tools like Multi-Criteria Decision Analysis (MCDA), the Analytic Hierarchy Process (AHP), and Decision Tree Analysis can assist in choosing the best strategies. These models help prioritise interventions such as resource distribution, vector control efforts, and public awareness campaigns, especially when dealing with uncertainties like limited resources or unpredictable case surges. The findings indicate that integrating these decision-making frameworks into public health planning can foster better coordination among policymakers, healthcare workers, and local authorities. This improved coordination can lead to quicker responses, more effective use of resources, and ultimately, a reduction in dengue cases and their impact on communities. The study emphasises that combining management science tools with traditional epidemiology isnt just helpfulits essential for strengthening outbreak preparedness. Plus, these approaches can be adapted to tackle other communicable diseases in India and similar settings worldwide, paving the way for smarter, more resilient public health systems. 2025, Indian Society for Malaria and Communicable Diseases. All rights reserved. -
Decision-making using regression analysis: a case study on Top Tier Holidays LLP
Research methodology: This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study. Case overview/synopsis: This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing. Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment. Complexity academic level: This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes. 2023, Emerald Publishing Limited. -
Decoding Big Data: The Essential Elements Shaping Business Intelligence
In today's Business Intelligence (BI) world, Big Data Analytics integration has become critical, transforming company strategy and decision-making processes. This study investigates the complex influence of Big Data on business intelligence, focusing on important drivers of this transition. It investigates how Big Data's improved data processing capabilities, integration of advanced analytics techniques such as machine learning, and real-time data insights enable businesses to make more informed decisions and achieve a competitive advantage. Furthermore, the paper emphasizes the importance of personalized consumer insights, operational savings, and strategic benefits obtained from predictive analytics when adopting Big Data for BI. 2024 IEEE. -
Decoding boomerang hiring: A suggestive framework to improve organizational efficiency
In an ever changing, volatile and dynamic business environment, efforts put by the human resources reflect the organizational efficiency. Organizations should always focus on maintaining smooth relations with the Alumni and Boomerangs as they play a crucial role in the expanding horizons of business. A positive word of mouth also helps in improving the goodwill and image of the company. It will encourage the prospective employees to view the organization in a positive light. Rehiring former employees is one of the mechanisms for recruitment used by a large number of corporations primarily because of the inherent advantage of added experience as well as savings in terms of cost of recruitment and training. The present study attempts to give an overview of Boomerang Hiring, the possible value additions being made in terms of Human Capital and Social Capital on basis of the type of respective organizations they are returning from. Additionally, the perspective of the rehired employee is also presented. The study is further enriched by quoting a few instances from the corporate world. The Rehiring Strategies tailored as per organizational requirements will lead towards holistic growth and development of the entity. 2020 SERSC. -
Decoding Breast Cancer Mutational Signatures: A Hybrid ElasticNetXGBoost Approach Using Gene Expression Data
TP53, PIK3CA, and MUC16 are somatic mutations that are useful in breast cancer progression and prognosis, but direct mutation profiling based on sequencing is not always practicable in practice. The data about gene expression can contain indirect transcriptomic patterns linked with mutational underlying states. This paper proposes an expression-based machine learning model to predict the status of mutations using METABRIC breast cancer cohort. Instead of directly estimating genetic changes, the suggested method estimates statistical relationships between transcriptomic phenotypes and binary somatic mutation states. A multi-stage gene features selection pipeline using variance filtering, mutual information ranking, and correlation pruning was used to reduce the number of genes (19,000). A hybrid predictive architecture was trained using these features that combined ElasticNet logistic regression and XGBoost that allowed balancing between linear regularization and nonlinear interaction modeling. The hybrid model with a combination of five-fold stratified cross validation yielded mean ROC-AUC of 0.94 (TP53), 0.92 (PIK3CA), and 0.90 (MUC16) with the stability of the calibration and equal error rates. Coefficient analysis and SHAP-based explanations were used to investigate the interpretability of the models to describe the expression patterns on mutation status. The suggested framework is a hypothesis-generating, complementary method of transcriptomic analysis, which must be reevaluated by external validation to determine the wider generalizability. 2026, International Journal of Prognostics and Health Management. All rights reserved.