Analytical Framework Research Articles

The Dynamics of African traditional justice systems: Perspectives and prospective

ABSTRACT This study investigates the dynamics of African traditional justice systems in four African countries: Sierra Leone, Malawi, Uganda, and Zimbabwe. These countries have a rich history of traditional justice systems deeply rooted in their respective societies. These systems are regarded as essential elements of African communities, embodying unique philosophies, rituals, and practices that have historically played a crucial role in resolving conflicts and fostering peace. The evolution of law and order in these African countries has been heavily influenced by these traditional justice systems, contributing to the establishment of a robust governance structure. By conducting an analysis of customary justice frameworks in the sub-Saharan African context, this study examines the perceptions of local populations regarding the traditional justice system as a legitimate institution. It explores how the traditional justice system can function as an alternative or complementary form of justice within traditional societies. Drawing on previous research conducted in Sierra Leone, Malawi, Uganda, and Zimbabwe, this study reveals the challenges associated with integrating traditional justice systems into the broader legal framework. Nevertheless, it concludes that the integration of these systems can contribute to a comprehensive approach to sustainable peace-building, conflict resolution, and the establishment of a just rule of law in these African countries.

Longitudinal Coadaptation of Older Adults With Wearables and Voice-Activated Virtual Assistants: Scoping Review.

The integration of smart technologies, including wearables and voice-activated devices, is increasingly recognized for enhancing the independence and well-being of older adults. However, the long-term dynamics of their use and the coadaptation process with older adults remain poorly understood. This scoping review explores how interactions between older adults and smart technologies evolve over time to improve both user experience and technology utility. This review synthesizes existing research on the coadaptation between older adults and smart technologies, focusing on longitudinal changes in use patterns, the effectiveness of technological adaptations, and the implications for future technology development and deployment to improve user experiences. Following the Joanna Briggs Institute Reviewer's Manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, this scoping review examined peer-reviewed papers from databases including Ovid MEDLINE, Ovid Embase, PEDro, Ovid PsycINFO, and EBSCO CINAHL from the year 2000 to August 28, 2023, and included forward and backward searches. The search was updated on March 1, 2024. Empirical studies were included if they involved (1) individuals aged 55 years or older living independently and (2) focused on interactions and adaptations between older adults and wearables and voice-activated virtual assistants in interventions for a minimum period of 8 weeks. Data extraction was informed by the selection and optimization with compensation framework and the sex- and gender-based analysis plus theoretical framework and used a directed content analysis approach. The search yielded 16,143 papers. Following title and abstract screening and a full-text review, 5 papers met the inclusion criteria. Study populations were mostly female participants and aged 73-83 years from the United States and engaged with voice-activated virtual assistants accessed through smart speakers and wearables. Users frequently used simple commands related to music and weather, integrating devices into daily routines. However, communication barriers often led to frustration due to devices' inability to recognize cues or provide personalized responses. The findings suggest that while older adults can integrate smart technologies into their lives, a lack of customization and user-friendly interfaces hinder long-term adoption and satisfaction. The studies highlight the need for technology to be further developed so they can better meet this demographic's evolving needs and call for research addressing small sample sizes and limited diversity. Our findings highlight a critical need for continued research into the dynamic and reciprocal relationship between smart technologies and older adults over time. Future studies should focus on more diverse populations and extend monitoring periods to provide deeper insights into the coadaptation process. Insights gained from this review are vital for informing the development of more intuitive, user-centric smart technology solutions to better support the aging population in maintaining independence and enhancing their quality of life. RR2-10.2196/51129.

HST SHEL: Enabling Comparative Exoplanetology with HST/STIS

The Hubble Space Telescope (HST) has been our most prolific tool to study exoplanet atmospheres. As the age of JWST begins, there are a wealth of HST archival data that are useful to strengthen our inferences from JWST. Notably, HST/Space Telescope Imaging Spectrograph (STIS), with its 0.3–1 μm wavelength coverage, extends past JWST’s 0.6 μm wavelength cutoff and holds an abundance of potential information: alkali (Na, K) and molecular (TiO, VO) species opacities, aerosol information, and the presence of stellar contamination. However, time-series observations with HST suffer from significant instrumental systematics and can be highly dependent on choices made during the transit fitting process. This makes comparing transmission spectra of planets with different data reduction methodologies challenging, as it is difficult to discern whether an observed trend is caused by differences in data reduction or underlying physical processes. Here we present the Sculpting Hubble’s Exoplanet Legacy (SHEL) program, which aims to build a consistent data reduction and light-curve analysis methodology and associated database of transmission spectra from archival HST observations. In this paper, we present the SHEL analysis framework for HST/STIS and its low-resolution spectroscopy modes, G430L and G750L. We apply our methodology to four notable hot Jupiters, WASP-39 b, WASP-121 b, WASP-69 b, and WASP-17 b, and use these examples to discuss nuances behind analysis with HST/STIS. Our results for WASP-39 b, WASP-121 b, and WASP-17 b are consistent with past publications, but our analysis of WASP-69 b differs and shows evidence of either a strong scattering slope or stellar contamination. The data reduction pipeline and tutorials are available on Github and Zenodo.

Dynamic path analysis for exploring treatment effect mediation processes in clinical trials with time-to-event endpoints.

Why does a beneficial treatment effect on a longitudinal biomarker not translate into overall treatment benefit on survival, when the biomarker is in fact a prognostic factor of survival? In a recent exploratory data analysis in oncology, we were faced with this seemingly paradoxical result. To address this problem, we applied a theoretically principled methodology called dynamic path analysis, which allows us to perform mediation analysis with a longitudinal mediator and survival outcome. The aim of the analysis is to decompose the total treatment effect into a direct treatment effect and an indirect treatment effect mediated through a carefully constructed mediation path. The dynamic nature of the underlying methodology enables us to describe how these effects evolve over time, which can add to the mechanistic understanding of the underlying processes. In this paper, we present a detailed description of the dynamic path analysis framework and illustrate its application to survival mediation analysis using simulated and real data. The use case analysis provides clarity on the specific exploratory question of interest while the methodology generalizes to a wide range of applications in drug development where time-to-event is the primary clinical outcome of interest.

Uncovering an emerging policy direction for Australian energy and future fuels using a “participatory decision-making” framework

IntroductionAn online deliberative engagement process was undertaken with members of the general public to understand what they value or would like to change about the energy system, within the broader context of decarbonizing Australia's energy networks, identifying a role for future fuels (hydrogen and biogas). Citizens developed a set of principles that could guide Australia's path toward a low-carbon energy future, reflecting on expectations they place upon energy transition. Next, citizens' principles were shared with policy-makers in government and policy-influencers from the energy industry using an online interactive workshop.MethodsThis study analyses policy-makers and -influencers response to citizens' guiding principles using the 'diamond of participatory decision-making' framework for analysis. Convergence and divergence in diverse, complex and rich views across cohorts and implications thereupon energy policy were identified.ResultsAlthough considerable alignment between multi-stakeholders' views was noted, key areas of divergence, or what is called the “groan zone” were easily identified in relation to social and environmental justice issues. This groan zone highlights the struggles that energy policy-makers face -the need to listen and respond to citizens' voices, vs. the need for practical and workable policies that also support overarching government or industry objectives.DiscussionPolicy making when the views of different stakeholders align is relatively straightforward. However, this is not the case where the expectations diverge. More creative measures will be needed to address divergent views and expectations whilst maintaining procedural fairness, in this case, using democratic deliberative engagement processes. While the use of deliberative processes is gaining momentum worldwide, particularly concerning climate change and energy transition policies, this paper also highlights the benefits of conducting a robust post facto analysis of the content of the processes. Areas of alignment, where policy can be made and implemented relatively easily without contention are identified. Other areas (such as making electrification mandatory) might be more complex or have unwanted negative social and environmental justice effects. Overall, this paper bridges an analytical gap between “expectation studies” and participatory research. By borrowing terminology from a participatory research framework we sharpen the concepts in “expectation studies” from a consensus, inclusion and diversity standpoint.

A Review of Feminist Legal Theory on Abortion Regulations for Rape Victims in Indonesia

The purpose of this study is to review and evaluate abortion regulations for rape victims, especially the requirements outlined in the Health Law and Government Regulation No. 61 of 2014 concerning Reproductive Health, using feminist legal theory as an analysis framework. This study uses an extra-doctrinal methodology outside the laws and regulations, emphasizing feminist legal theory as the basis of thought. The findings suggest that abortion regulations for rape victims still largely ignore women's life experiences and put them in difficult situations. The marginalization of women victims of rape in accessing safe abortion is a consequence of the gestational age limit for abortion, which fails to acknowledge the different experiences of women and treat them as a homogeneous group. To effectively address this issue, it is critical to involve the participation of women who have experienced rape, including victims of sexual violence, in the development of policies related to abortion for rape victims.

An integrated framework for rainfall prediction and analysis using a Stacked Heterogeneous Ensemble Model (SHEM)

Recent climate conditions in India have led to numerous disasters, including floods, overflowing rivers, broken dams, and reduced vegetation. Machine learning aims to train models to make predictions based on historical data. Although many studies have focused on predictions and model building, this research introduces a novel integrated framework using a Stacked Heterogeneous Ensemble Model (SHEM). It aims to enhance prediction accuracy by employing another novel approach for imputing missing values, the Variable Specific Hot Deck (VSHD) imputation method. The outcomes were contrasted with established machine learning techniques, including random forest, decision tree, k-nearest neighbor, and support vector machine. After completing the imputation, we proceeded to implement the SHEM model. Real-time climate data for the Cuddalore location in Tamil Nadu state, South India was collected from the NASA Power Access viewer portal to verify the accuracy level of the proposed model. Performance analysis indicates that the proposed imputation outperforms all four alternative models with a 30% average improvement in accuracy. Moreover, the developed SHEM model has a reduced RMSE value of 0.321 and an R-squared value of 0.952 and shows a 9% improvement in accuracy compared to the base model’s performance. Furthermore, these prediction results are compared to an LSTM (Long Short Term Memory), a deep learning model to calculate accuracy and loss, showing that the proposed achieves high accuracy and significant loss during validation. The obtained results can serve as a guideline for atmospheric scientists and various weather forecasting applications, helping them to choose the most appropriate machine learning method for their prediction task.

A shadowy negotiation involving dams and its fiscal and legal implications: a Portuguese case study

Purpose This paper aims to explore the intricate and controversial sale of six hydroelectric dams in the Douro hydrographic basin by Energias de Portugal (EDP), a prominent Portuguese energy company, to a French Consortium – ENGIE. The transaction, completed at the end of 2020, has sparked significant debate and scrutiny within the Portuguese legal and fiscal spheres due to its corporate and budgetary manoeuvres. The crux of the controversy lies in the complex corporate restructuring strategies used by EDP and the acquiring consortium to execute this transaction. These strategies, aimed at achieving tax neutrality, effectively circumvented the traditional tax liabilities typically associated with large-scale asset transfers. The paper delves into the legal intricacies of this operation, scrutinising the application of taxes such as stamp duty, corporate income tax, value added tax and property transfer tax, which were, in theory, applicable to the transaction. Furthermore, this study examines the broader implications of the deal, particularly concerning the principle of tax neutrality in corporate restructurings, the enforcement of anti-abuse clauses and the economic substance over legal form doctrine. Design/methodology/approach This study is based on secondary data supported by publicly reported evidence. Findings This case study highlights the challenges in taxing corporate transactions in the modern financial landscape and reflects these corporate manoeuvres' societal and ethical considerations. Originality/value Through an analysis of legal frameworks, corporate strategies and tax policies, this paper provides a comprehensive understanding of the transaction and its implications, offering insights valuable to legal professionals, policymakers and scholars in corporate law, taxation and business ethics.

Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy

Fluorescence microscopic imaging is essentially a convolution process distorted by random noise, limiting critical parameters such as imaging speed, duration, and resolution. Though algorithmic compensation has shown great potential to enhance these pivotal aspects, its fidelity remains questioned. Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity. Our approach employs a multi-resolution analysis (MRA) framework to extract the two main characteristics of fluorescence images against noise: across-edge contrast, and along-edge continuity. By constraining the two features in a model-solution framework using framelet and curvelet, we develop MRA deconvolution algorithms, which improve the signal-to-noise ratio (SNR) up to 10 dB higher than spatial derivative based penalties, and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference. We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity, enabling accomplishments of more challenging imaging tasks.

A framework for analysis of stakeholder dynamics and value creation in industrial maintenance projects: the stakeholder ipot

AbstractThis paper proposes a methodological approach that can be applied in practice for evaluating stakeholder dynamics and assessing projects against appropriate value propositions within an industrial maintenance project context. A conceptual framework is proposed and is demonstrated through a case analysis. It is expected that the proposed methodology, the Stakeholder Interdependent Performance Opportunities and Threats, (Stakeholder iPOT), can advance project management practice by offering a mechanism for analysing stakeholder expectations and responses to the opportunities and threats that different project events present. This study highlights the need for continued investigation not only within the context of industrial maintenance projects but also in other sectors to improve our understanding and ability to effectively manage stakeholder dynamics.

Young adults with kidney failure lived experiences of kidney replacement therapy decision-making.

Young adults living with kidney failure make decisions to select a kidney replacement therapy choice in partnership with healthcare professionals. However, little is known about how they experience kidney replacement therapy treatment decision-making and the impact this has on their well-being. To explore young adults living with kidney failure experiences of treatment decision-making. The treatment decision-making investigated is about the choice of dialysis and/or kidney transplant options. A qualitative interpretive hermeneutic phenomenology study. Purposeful sampling was used to recruit young adults with kidney failure from social media, electronic media such as local kidney group websites and word of mouth. Semistructured interviews were conducted with (n = 18) participants aged 18-30 years. Inductive analysis of the data were performed using Braun and Clarke's thematic analysis framework. The five themes generated were (1) awareness and anticipation of future kidney replacement therapy decision; (2) health information and education; (3) engaging in decision-making, support and choices; (4) implementation of kidney replacement therapy and transitioning into the new normal life and (5) the impact of decision-making and choice on well-being. Decision-making significantly affected young adults' psychosocial and mental well-being. Young adults had unmet informational and decisional needs and struggled to cope due to lack of support. A four-talk model, with an implement talk phase added to the existing three-talk (team talk, option talk, decision talk) shared decision-making model, would promote a focus on the implementation of choice and support the transitioning from previous life to long-term dependence on treatment.

Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis.

The escalating global challenges of population growth, climate crisis, and resource depletion have intensified water scarcity, emphasizing the critical role of wastewater treatment (WWT) in environmental preservation. While discharging untreated wastewater poses extinction risks to various species, effective WWT operations are indispensable for ecosystem continuity and sustainable water sources. Recognizing the complexity of WWT management, this study delves into the potential of artificial intelligence (AI) in strategic planning and decision-making within the WWT domain. Through a comprehensive SWOT analysis, this study evaluates the strengths, weaknesses, opportunities, and threats associated with AI integration in WWT processes. Utilizing the SWOT analysis framework, key criteria are identified, and their importance weights are assessed via the interval-valued neutrosophic analytical hierarchy process (IVN-AHP). According to analysis, the strengths in WWT are crucial, but potential opportunities and threats should not be ignored. The results of the study highlight several key findings regarding the integration of AI in WWT processes. While concerns about the reduction in human resources and potential unemployment, as well as the activation time and high energy consumption of AI systems, are identified as significant challenges, the study underscores the success of AI in data analytics as a strong aspect. Specifically, advanced data analysis techniques and the ability to proactively prevent problems emerge as important strengths of AI in WWT. WWT operators and practitioners are encouraged to prioritize the adoption of advanced data analysis techniques and proactive problem-solving strategies to maximize the effectiveness of AI integration in WWT processes.

Stability of a continuous/discrete sensitivity model for the Navier–Stokes equations

AbstractThis work presents a comprehensive framework for the sensitivity analysis of the Navier–Stokes equations, with an emphasis on the stability estimate of the discretized first‐order sensitivity of the Navier–Stokes equations. The first‐order sensitivity of the Navier–Stokes equations is defined using the polynomial chaos method, and a finite element‐volume numerical scheme for the Navier–Stokes equations is suggested. This numerical method is integrated into the open‐source industrial code TrioCFD developed by the CEA. The finite element‐volume discretization is extended to the first‐order sensitivity Navier–Stokes equations, and the most significant and original point is the discretization of the nonlinear term. A stability estimate for continuous and discrete Navier–Stokes equations is established. Finally, numerical tests are presented to evaluate the polynomial chaos method and to compare it to the Monte Carlo and Taylor expansion methods.

Media portrayal of sexual violence in Pakistan: A critical discourse analysis of the Lahore-Sialkot motorway incident

Sexual violence against women is one of the serious social concerns that still persist across the world in its various forms and manifestations. This study talks about the portrayal of the gang-rape of a woman in Pakistani English Print media who was travelling on the Lahore-Sialkot Motorway when her car ran out of petrol where two armed men came along and raped her. Grounded in the theoretical framework of Critical Discourse Analysis (CDA), Transitivity analysis from Halliday's systemic functional linguistics is utilized to explore the language used by leading Pakistani English newspapers to report the incident. Moreover, the study examines how print media affects the opinion of the people towards the incidents of sexual violence against women in Pakistan. These discursive features of a language enable us to see deep into the thought-processes of the people of a society. The paper concludes that reporting on violence against women shapes and is shaped by the approach of the people towards women and the forms of violence against them.

Disability Identity Development Scale: A validation study among college students with disabilities.

(a) To investigate the construct validity of the DIDS scores at the item and factor level using item response theory (IRT) and confirmatory factor analysis (CFA); (b) to investigate convergent validity of the DIDS scores with related constructs; and (c) to determine the reliability of each DIDS factor. A convenience sample of 210 undergraduate college students with disabilities enrolled in a west coast 4-year public university participated in this study. Measures included the DIDS, the University Belongingness Questionnaire, and the College Self-Efficacy Inventory. Data were analyzed using the Rasch IRT framework, CFA, Omega reliability, and correlational analyses. IRT and CFA results revealed the data fit a 36-item, four-factor DIDS structure. Three items in the factor measuring adoption of disability community values items were easier, and four items in the factor measuring contribution to the disability community were difficult as compared with other DIDS items. Omega reliability analyses showed strong reliability for each DIDS factor. Correlation analyses found convergent and discriminant validity evidence for the DIDS with correlations with the University Belongingness Questionnaire and College Self-Efficacy Inventory in the hypothesized direction and magnitude. Findings provide evidence for Forber-Pratt and colleagues' theoretically and empirically derived DIDS, advancing the research, measurement, and practical application of disability identity development. Results are aligned with Forber-Pratt's psychosocial model of disability identity theoretical framework. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A hybrid approach to analysing large scale surveys: individual values, opinions and perceptions

High-dimensional large scale surveys enable broad research capabilities and potential insight. However, when dealing with the intrinsic complexity of social science, the underlying knowledge engineering process may play a critical role and require to consider the characteristics and peculiarities of a given problem in context. This study proposes an analysis framework based on clustering techniques, which have been applied to discover patterns among a number of abstracted features resulting from selected attributes of the World Values Survey (WVS). As an assumption, such features have been softly classified as values, opinions and perceptions, based on their theoretical likelihood to change along the time. From a more philosophical perspective, this work assumes hybrid practices as there is no pre-formulated theory but rather an attempt to discover patterns and new knowledge from data. Given the relatively manageable dimensionality of the input dataset, the feature selection has been performed according to an application-oriented approach (rather than driven by statistical analysis) to establish a more comprehensive and consistent research framework. Among the main findings, a symbiotic relationship between the perception of satisfaction and of financial stability, as well as between the perception of security and of happiness, in addition to more complex patterns involving traditional values (e.g. family and religion), politics and believes with an impact on society. Last but not least, despite its holistic focus, the study has allowed the identification of few research gaps and, therefore, potential further research direction in the broad domain of Social Sciences.

Vertical Spatial Differentiation and Influencing Factors of Rural Livelihood Resilience: Evidence from the Mountainous Areas of Southwest China

Rural livelihood resilience (RLR) is significant for the sustainability of rural areas, farmers and agriculture. This research takes the mountainous areas of Southwest China as the study areas and integrates the buffer, adjustment and renewal capabilities to construct a RLR analysis framework and evaluation indicator system. The RLR of 234 sample counties was evaluated using set pair analysis, and the influencing factors of RLR were investigated using Geodetector. The aim is to scientifically analyze the vertical spatial differentiation and influencing factors of RLR and fully explore the risk-resistant potential of rural livelihood systems in mountainous areas. The results show that (1) From 2000 to 2020, RLR increases significantly, but the level is still low, and its structure shows a primary pattern of “buffer capability > adjustment capability > renewal capability”. (2) There is no significant negative correlation between RLR and terrain gradients, especially in the middle and low mountains. RLR in high mountainous areas is significantly lower than in low and medium mountainous areas, but the gap is narrowing. There are no significant gaps in RLR and various capabilities between low and medium mountain areas. (3) Economic development, non-agricultural industries and grassroots autonomous organizations are essential determinants for RLR. Economic development, grassroots autonomous organizations and transportation conditions are more important in low and medium mountain areas, while non-agricultural industries and medical conditions contribute more to high mountainous areas. (4) To enhance the RLR, policy recommendations should place a strong emphasis on extending the agricultural industry chain, improving rural production and living infrastructure and strengthening the supply of high-quality social public services. The findings can provide a scientific basis for governments to implement rural revitalization strategies and improve farmers’ well-being, as well as practical guidance for enhancing the risk resistance ability of underdeveloped mountainous rural areas.

Picturing immediate echolalia within the context of autism: Examining its formats, actions and patterns under conversation analysis

ABSTRACT Echolalia, a prevalent feature of Autism Spectrum Disorders (ASD), has been extensively debated among behaviourists and developmental researchers for decades, long segmenting clinical work within the context of autism. This qualitative study aimed to explore the interactive underpinnings of immediate echolalia naturally occurring in dyadic conversation between autistic individuals and clinicians, employing turn-by-turn and sequence-by-sequence analysis within the framework of Conversation Analysis (CA). The results revealed that varying the complete-incomplete-transformed format, echolalia helped participants a) express their emotions, b) automatically associate conversation, c) organise their response, d) maintain conversational reciprocity, and e) assist with request initiation. Within the context of echolalia, the dynamics of conversation exhibited blocking, diverting, or affiliating patterns. The current study provides insights into the interactive traits of immediate echolalia and underscores the potential utility for clinical therapists to employ the echoic sources in clinical intervention.

Dispersion Analysis of Plane Wave Propagation in Lattice-Based Mechanical Metamaterial for Vibration Suppression

Phononic crystals based on lattice structures provide important wave dispersion characteristics as band structures, showing excellent compatibility with additive manufacturing. Although the lattice structures have shown the potential for vibration suppression, a design guideline to control the frequency range of the bandgap has not been well established. This paper studies the dispersion characteristics of plane wave propagation in lattice-based mechanical metamaterials to realize effective vibration suppression for potential aerospace applications. Triangular and hexagonal periodic lattice structures are mainly studied in this paper. The influence of different geometric parameters on the bandgap characteristics is investigated. A finite element approach with Floquet–Bloch’s principles is implemented to effectively evaluate the dispersion characteristics of waves in lattice structures, which is validated numerically and experimentally with a 3D-printed lattice plate. Based on numerical studies with the developed analysis framework, the influences of the geometric parameters of lattice plate structures on dispersion characteristics can mainly be categorized into three patterns: change in specific branches related to in-plane or out-of-plane vibrations, upward/downward shift in frequency range, and drastic change in dispersion characteristics. The results obtained from the study provide insight into the design of band structures to realize vibration suppression at specific frequencies for engineering applications.

Efficient Governance for Indian States using Big Data

Big data integration with e-governance offers revolutionary potential to improve the effectiveness and responsiveness of state government operations in India. In addition to highlighting important research gaps and outlining complete solutions, this article investigates the role of big data technologies in modernizing e-governance frameworks. Our approach includes a comprehensive assessment of the literature as well as the creation of theoretical frameworks for sentiment analysis, data integration, predictive analytics, and real-time data processing. We suggest cutting-edge uses for these frameworks in disaster management, law enforcement, traffic management, healthcare, and environmental monitoring. The findings of this study have implications for better governance, better decision-making, and better public services. The application of these frameworks in pilot projects in different states, comparative analyses to find best practices, and ongoing technological adaptation to changing governance requirements are some of the future paths that these projects will pursue. The research highlights the importance of implementing big data to achieve an efficient, transparent, and flexible government in India.