Nova Integrata: Journal of Multidisciplinary Studies
Nova Integrata: Journal of Multidisciplinary Studies is a peer-reviewed academic journal dedicated to fostering cross-disciplinary research and knowledge integration. The journal serves as a platform for scholars, researchers, and practitioners from diverse academic fields—including social sciences, natural sciences, humanities, engineering, business, health sciences, and technology—to present innovative studies that transcend traditional subject boundaries. By promoting dialogue between varied disciplines, Nova Integrata aims to address complex global challenges and inspire holistic solutions.
Advancements in Structural Engineering: Designing Earthquake-Resilient Buildings for Urban Environments
As global urbanization intensifies and seismic risks increase in densely populated regions, developing earthquake-resilient buildings has become a critical priority in structural engineering. This study provides a comprehensive analysis of advancements in seismic design methodologies, innovative materials, and smart technologies implemented between 2010-2023 across high-seismicity urban regions including California, Japan, Chile, New Zealand, and Turkey. Employing a problem-based research methodology, the investigation synthesizes performance data from 850 instrumented buildings across 45 major earthquakes (magnitude 6.0-9.0), laboratory testing of 120 structural components, and computational modeling of 5,000 building configurations. Results demonstrate that modern seismic design approaches—including base isolation, energy dissipation devices, and rocking wall systems—reduce structural damage by 60-85% compared to conventional fixed-base construction during major seismic events. Performance analysis reveals that buildings incorporating seismic isolation systems experienced peak inter-story drift reductions of 70-90% and floor acceleration reductions of 60-80% during the 2011 Christchurch and 2017 Mexico City earthquakes. Material innovations show that high-performance fiber-reinforced concrete (HPFRC) increases energy dissipation capacity by 300-400% compared to conventional concrete, while shape memory alloy (SMA) reinforcement maintains 95% of its original strength after experiencing 7% strain. Post-earthquake functionality assessments indicate that resilient buildings achieve 85-95% immediate occupancy ratings compared to 15-30% for conventional buildings following major seismic events. However, cost-benefit analysis reveals that advanced seismic technologies increase initial construction costs by 8-25%, though lifecycle cost savings from reduced downtime and repair needs yield benefit-cost ratios of 3.5-6.2 over 50-year building lifespans. Implementation barriers include code compliance challenges (affecting 35% of innovative systems), skilled labor shortages (60% of contractors lack specialized training), and public perception gaps regarding cost versus safety trade-offs. This research concludes that while seismic resilience technologies have advanced substantially, their widespread urban implementation requires integrated approaches combining performance-based design, regulatory innovation, workforce development, and economic incentives to create truly earthquake-resilient cities.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
The Future of Autonomous Vehicles: Analyzing the Technological and Ethical Challenges of Self-Driving Cars
The emergence of autonomous vehicles (AVs) promises to revolutionize transportation systems, potentially increasing road safety, improving mobility access, and transforming urban landscapes. This study provides a comprehensive analysis of the technological capabilities, ethical dilemmas, and societal implications of self-driving cars through a mixed-methods approach incorporating simulation data, accident reports, public opinion surveys, and policy analyses from 2015-2023. Results indicate that while Level 4 autonomous systems demonstrate 94% fewer accidents than human drivers in controlled environments, they face significant challenges in complex urban scenarios, with disengagement rates increasing from 0.02 to 1.8 interventions per 100 miles as environmental complexity rises. Ethical analysis reveals that public preferences for autonomous vehicle decision-making in unavoidable accident scenarios vary significantly across cultures, with Western populations preferring utilitarian algorithms (minimizing total harm) at 65% approval, while Eastern populations show stronger preference for protective algorithms (prioritizing vehicle occupants) at 58% approval. Technological assessments indicate that sensor fusion systems achieve 99.7% object detection accuracy in daylight conditions but degrade to 85.3% in heavy rain and 79.1% in snow. Cybersecurity vulnerabilities pose substantial risks, with penetration testing revealing successful remote takeover of vehicle controls in 17% of tested systems. Economic projections suggest AV deployment could eliminate 2.7-3.5 million driving jobs in the United States alone by 2040 while creating 0.8-1.2 million new positions in fleet management, remote supervision, and infrastructure maintenance. Legal frameworks remain fragmented across 32 studied jurisdictions, with liability assignment being the most contentious unresolved issue. This research concludes that while autonomous vehicles offer transformative potential, their responsible deployment requires addressing persistent technological limitations, establishing ethical and legal consensus, implementing robust cybersecurity protocols, and managing profound socioeconomic transitions through coordinated policy interventions and public engagement.
A Study on the Impact of Nanotechnology in Drug Delivery Systems: Innovations in Cancer Treatment
The integration of nanotechnology into drug delivery systems represents a paradigm shift in oncology, offering unprecedented opportunities to enhance therapeutic efficacy while minimizing systemic toxicity. This study conducts a comprehensive quantitative analysis of nanocarrier-based drug delivery systems (DDS) for cancer treatment, evaluating their clinical impact, technological innovations, and translational challenges. Employing a problem-based research methodology, the investigation synthesizes data from clinical trials (2005-2023), preclinical studies, and market analyses, focusing on four principal nanocarrier platforms: liposomes, polymeric nanoparticles, dendrimers, and inorganic nanoparticles. Results demonstrate that nano-enabled drug delivery enhances tumor accumulation by 10-100 fold through enhanced permeability and retention (EPR) effects and active targeting, with median drug concentrations in tumor tissue reaching 5-8% of injected dose compared to 0.1-0.5% for conventional chemotherapy. Clinical outcomes show significant improvements: nanoparticle albumin-bound paclitaxel (nab-paclitaxel) increased response rates in metastatic breast cancer by 33% compared to solvent-based paclitaxel, while reducing neurotoxicity incidence by 28%. However, translation remains challenging, with only 8.7% of nanomedicine candidates progressing from Phase I to FDA approval. Critical barriers identified include batch-to-batch variability (coefficient of variation >15% for 42% of formulations), scale-up complexities, and immunological recognition leading to accelerated blood clearance. Economic analysis reveals that nanomedicines command a 3-8x price premium over conventional counterparts, though value-based assessments show improved cost-effectiveness in specific indications. Emerging innovations—particularly stimuli-responsive "smart" nanoparticles and combination immunotherapy platforms—demonstrate potential to overcome current limitations. This research concludes that nanotechnology fundamentally enhances cancer pharmacotherapy, but realizing its full potential requires addressing manufacturing standardization, regulatory harmonization, and healthcare system integration challenges within a patient-centered framework.
The Development of Biodegradable Plastics: Challenges and Opportunities for Sustainable Packaging Solutions
The global plastic pollution crisis, driven primarily by single-use packaging, demands urgent transition toward sustainable alternatives. This study provides a comprehensive quantitative analysis of biodegradable plastics, examining their technical performance, environmental impact, economic viability, and market adoption barriers. Employing a problem-based research methodology, the investigation synthesizes data from over 200 scientific studies, industry reports, and market analyses spanning 2010-2023, focusing on major biodegradable polymer families: polylactic acid (PLA), polyhydroxyalkanoates (PHA), starch-based blends, and polybutylene adipate terephthalate (PBAT). Results indicate that while biodegradable plastics offer significant end-of-life advantages—with complete biodegradation achieved in industrial composting within 12-26 weeks—they face substantial challenges in mechanical properties, barrier performance, and cost competitiveness. Current biodegradable plastics exhibit 15-40% lower tensile strength and 50-70% higher oxygen transmission rates compared to conventional polyethylene, limiting their application scope. Economically, they remain 2.2-4.5 times more expensive than conventional plastics at commercial scale. However, life cycle assessment reveals that optimized production scenarios can reduce carbon footprints by 25-60% compared to petroleum-based counterparts. Market analysis shows adoption concentrated in food service (42% of applications) and retail packaging (28%), with Europe leading regulatory-driven implementation. Critical barriers identified include inconsistent certification standards, limited industrial composting infrastructure (available to only 35% of global urban populations), and consumer confusion regarding proper disposal. The study identifies promising opportunities in next-generation materials, particularly polymer blends and nanotechnology enhancements, which show potential to close the performance gap while maintaining biodegradability. This research concludes that biodegradable plastics represent a necessary but insufficient solution alone; their successful integration requires complementary advances in circular economy infrastructure, regulatory harmonization, and consumer education to realize their potential within a holistic sustainable packaging strategy.
Exploring the Role of Artificial Intelligence in Predicting Climate Change Patterns and Mitigating Environmental Disasters
The escalating frequency and intensity of climate-driven environmental disasters necessitate advanced predictive capabilities and proactive mitigation strategies. This research investigates the transformative role of Artificial Intelligence (AI) in enhancing the accuracy of climate pattern predictions and optimizing disaster response mechanisms. Through a quantitative problem-based methodology, the study evaluates the performance of various AI models—including deep learning neural networks, ensemble methods, and reinforcement learning algorithms—against traditional climate modeling techniques. The analysis incorporates multi-source data from satellite remote sensing (MODIS, Sentinel), global climate models (CMIP6), historical disaster databases (EM-DAT), and real-time sensor networks spanning 2010-2023. Results demonstrate that AI-driven models reduce prediction errors for extreme weather events by an average of 42% compared to conventional physical models, with lead times for hurricane track forecasting improved by 36 hours. In wildfire prediction, convolutional neural networks (CNNs) achieved 94% accuracy in forecasting ignition risk zones 72 hours in advance by integrating meteorological, topographic, and vegetation data. For flood mitigation, reinforcement learning algorithms optimized reservoir release schedules, reducing potential flood damage by 28% while maintaining water security. The study also reveals that hybrid AI-physical models significantly improve long-term climate pattern projections, reducing uncertainty in regional temperature and precipitation forecasts by 31%. However, challenges persist regarding data quality, model interpretability ("black box" problem), and computational resource requirements. The research concludes that AI is not a replacement but a powerful augmentation to existing climate science frameworks, enabling more precise, timely, and actionable intelligence for policymakers and disaster management agencies. Strategic integration of AI into climate resilience planning can substantially reduce human and economic losses, though it requires sustained investment in data infrastructure, interdisciplinary collaboration, and ethical governance frameworks.
Examining the Role of Museums in Preserving Cultural Heritage and Promoting National Identity
Museums occupy a central position in the preservation of cultural heritage and the construction of national identity, functioning as spaces where collective memory, historical interpretation, and public engagement intersect. This study examines the role of museums in safeguarding cultural heritage while actively shaping national identity, using a convergent mixed-methods research design. Quantitative data were collected through structured visitor surveys, while qualitative insights were obtained from interviews with curators, educators, and visitors, alongside exhibition content analysis and observational methods. The findings reveal that museums significantly influence visitors’ understanding of national history, cultural authenticity, and collective belonging through curated narratives, storytelling techniques, and inclusive exhibition practices. Results further demonstrate that emotional engagement, perceived authenticity, and memorability of museum experiences strongly contribute to visitor satisfaction, revisit intentions, and positive word-of-mouth. The integration of digital and AI-based technologies emerges as an increasingly important factor in enhancing engagement and accessibility, although institutional strategies and curatorial perspectives remain decisive in shaping national narratives. Overall, the study highlights museums as active agents in nation-building rather than neutral repositories, emphasizing their capacity to balance heritage preservation with evolving social, political, and technological contexts. The research contributes empirical evidence to museum studies and heritage scholarship by clarifying how museums mediate between cultural preservation, public experience, and national identity formation.
Copyright (c) 2025 Farzana Iqbal, Kamran Ali (Author) https://creativecommons.org/licenses/by-nc/4.0/deed.en
The Representation of Race and Ethnicity in Modern Cinema: A Critical Study of Hollywood's Racial Dynamics
Racial and ethnic representation in modern cinema plays a crucial role in shaping public perceptions of identity, power, and social belonging. This study critically examines patterns of racial and ethnic representation in contemporary Hollywood films using a mixed-methods approach that integrates quantitative content analysis with qualitative critical discourse analysis. Drawing on a large corpus of films spanning multiple decades, the research analyzes casting composition, screen-time allocation, narrative agency, stereotypical portrayals, genre variation, and industry participation. The findings reveal persistent structural inequalities in representation, characterized by the dominance of White-centered narratives, limited agency afforded to minority characters, and the continued circulation of racial stereotypes despite gradual improvements over time. Quantitative results further demonstrate uneven progress across genres and award recognition categories, as well as weak alignment between industry diversity policies and actual representational outcomes. Audience reception and sentiment analysis indicate increasing awareness of representational disparities, though such awareness has yet to translate into systemic change. By providing empirically grounded evidence across multiple dimensions of cinematic production and reception, this study contributes to media studies and critical race scholarship by illuminating how Hollywood continues to negotiate, resist, and occasionally transform racial representation in the twenty-first century.
Copyright (c) 2025 Sana Rauf, Arif Mehmood (Author) https://creativecommons.org/licenses/by-nc/4.0/deed.en
The Ethics of Artificial Intelligence in Literature: Analyzing the Portrayal of AI in Science Fiction
The ethical implications of artificial intelligence (AI) have become a central concern in contemporary technological discourse, yet public understanding of these issues is often shaped by fictional representations rather than real-world applications. This study examines how science fiction literature portrays the ethics of artificial intelligence and evaluates the relevance of these portrayals to contemporary AI governance and policy debates. Using a systematic review and mixed-methods content analysis, a diverse corpus of science fiction texts was analyzed to identify recurring ethical themes, AI typologies, narrative roles, and governance models. The results reveal that science fiction consistently foregrounds ethical concerns related to autonomy, control, surveillance, bias, moral responsibility, and human identity, often emphasizing dystopian or cautionary narratives. While speculative depictions frequently focus on superintelligent or anthropomorphic AI, many narratives also anticipate present-day ethical challenges such as algorithmic bias, mass surveillance, and accountability gaps. Quantitative analysis demonstrates a temporal shift in ethical focus, moving from early concerns about automation and control toward contemporary themes of governance, transparency, and human-centric AI. By systematically mapping these ethical representations, this study demonstrates that science fiction serves as a critical cultural space for ethical reflection, offering valuable insights that can inform responsible AI development and policy frameworks. The findings contribute to interdisciplinary scholarship by bridging literary analysis with applied AI ethics and regulatory discourse.
Copyright (c) 2025 Huma Siddiqui, Danish Rahman (Author) https://creativecommons.org/licenses/by-nc/4.0/deed.en
AI-Driven Genomic Analysis for Identifying Novel Biomarkers in Precision Medicine
This study explores the potential of artificial intelligence (AI)-driven genomic analysis in identifying novel biomarkers to advance the field of precision medicine. By leveraging machine learning and deep learning models across multi-omic datasets, including genomic, transcriptomic, and proteomic data, we demonstrate significant improvements in predictive accuracy, scalability, and biological interpretability compared to traditional biomarker discovery methods. The integration of heterogeneous data sources enabled the detection of subtle molecular patterns associated with disease progression and therapeutic response, while explainable AI techniques ensured that the results remained biologically meaningful and clinically relevant. Results indicate that AI-driven approaches reduced false discovery rates, improved classification of patient subgroups, and uncovered candidate biomarkers particularly valuable in oncology and rare disease contexts. Furthermore, the scalability of these models allows for application to large cohorts, facilitating the translation of biomarker discoveries into population-level precision health initiatives. The findings underscore the transformative role of AI in enabling patient stratification, targeted therapies, and personalized treatment strategies. While challenges related to algorithmic bias, data harmonization, and clinical validation persist, this research highlights that AI-driven biomarker discovery is an essential step toward realizing the full promise of predictive, preventive, and personalized medicine.
Artificial intelligence, genomic analysis, biomarker discovery, precision medicine, multi-omics, personalized healthcare
Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
The Role of Machine Learning in Remote Patient Monitoring and Telemedicine for Chronic Disease Management
The present study explores the integration of machine learning (ML) approaches into remote patient monitoring (RPM) and telemedicine platforms, with a focus on chronic disease management. By analyzing multi-modal health data, including wearable sensor outputs, electronic health records, and teleconsultation logs, we developed predictive frameworks to improve early detection, continuous monitoring, and personalized interventions for patients with chronic illnesses. Results demonstrated that ML-enhanced monitoring significantly improved accuracy in detecting early signs of disease deterioration compared to conventional methods, with models achieving superior predictive performance in forecasting adverse events and hospital readmissions. Furthermore, clustering and classification algorithms facilitated patient stratification, allowing for more efficient allocation of healthcare resources. The integration of ML into telemedicine also demonstrated a reduction in patient wait times, enhanced continuity of care, and improved adherence to treatment protocols. The findings highlight that AI-driven RPM not only strengthens clinical decision-making but also enhances patient empowerment by providing actionable insights through user-friendly digital platforms. Collectively, the results emphasize that the use of machine learning in telemedicine has the potential to revolutionize chronic disease management by delivering proactive, data-driven, and patient-centered healthcare solutions.
machine learning, telemedicine, remote patient monitoring, chronic disease, predictive healthcare, digital health
AI Applications in Improving Public Service Delivery Efficiency
This paper explores how artificial intelligence (AI) can help to improve the efficiency of the public service delivery system by taking a closer look at the opportunities offered by emerging technologies to improve the openness of operations, operational processes, and citizen happiness. The study reveals that the platforms by AI enabled reduction in processing time, the optimal utilization of resources, and a decrease in administrative errors by taking cues of both the quantitative performance metrics and qualitative judgments. The outcomes also indicate that machine learning models will make more accurate predictions, something that assists the planning of the institutions that offer services and respond in a timely manner. There is a difference however, automation simplifies workflows and as a result efficiency and cost effectiveness can be shown to have improved. The findings also indicate that, the use of AI makes things more accountable and transparent, as data-driven decision-making reduces the likelihood of making mistakes and generates trust in government. However, the paper indicates that effective AI application requires effective data governance and ethical protection and that organizations must be ready to transform into new technological paradigms. According to the study, AI can transform the way in which the delivery of the public services should take place on a large scale so that they become lean, accessible, and centered on the needs of citizens. Nevertheless, it is only possible in case of powerful institutional frameworks, which should guide it and its outcomes on society are continuously verified.
Artificial Intelligence, Public Service Delivery, Efficiency, Governance, Automation, Transparency
AI-Driven Analysis of Cross-Cultural Communication Patterns
In the present study, the artificial intelligence capabilities are tested in assessing cross-cultural patterns of communication based on a mixed-method experimental design. The data were collected in a multimodal form and included text transcripts, audio material, and paralinguistic cues of individuals of diverse cultural backgrounds. Audio analysis and Natural Language Processing were applied on preprocessing pipelines, and to sort communication styles Support Vector Machines, Random Forests, and BiLSTMs were employed. Statistical tests (ANOVA, correlation matrix) proved that considerable differences occurred between the cultural masses, and the attitude polarity, the use of pronouns, and the variation in intonation were discovered as the main discriminators. The results reveal that BiLSTM achieved the best classification accuracy unlike SHAP interpretability that confirmed the relevance of the emotional and linguistic features on the prediction of cultural communication tactics. There were also differences in visualizations of how polite people, how successful a conversation is, and how inter- and intracultural dynamics operate. These findings were supported by qualitative theme analysis, further enriching culture and giving substantiating insight. The findings of all the studies show that AI-based algorithms can be successfully used to detect and measure interculture communication strategies, thus both theorizing and practically introducing the field of communication, as well as utilizing such methodologies in education, business and foreign relations across the globe.
Artificial Intelligence, Cross-Cultural Communication, Machine Learning, Multimodal Analysis, Intercultural Studies, Communication Patterns
The Impact of Peer-to-Peer Lending Platforms on Financial Inclusion
Peer-to-peer (P2P) lending platforms have emerged as innovative financial technologies that directly connect borrowers and lenders, bypassing traditional banking intermediaries. This study investigates the impact of P2P lending on financial inclusion using a mixed-method research design that integrates quantitative data analysis with qualitative case studies. Quantitative analysis examines loan approval rates, borrower demographics, default probabilities, and interest rate structures across P2P platforms relative to conventional banks. Logistic regression models are applied to identify the determinants of credit access and repayment behaviors. Complementing this, qualitative data derived from interviews and case studies of borrowers and lenders provide insights into perceptions of fairness, trust, transparency, and digital accessibility. The integrated findings indicate that P2P lending significantly improves access to credit for underserved groups, including micro, small, and medium enterprises (MSMEs), women entrepreneurs, and rural borrowers, while also offering competitive interest rates compared to traditional banks. However, challenges such as higher default risks, limited digital literacy, and regulatory gaps highlight the need for robust oversight and borrower protection mechanisms. Overall, the study concludes that P2P lending platforms are not substitutes but complements to traditional finance, and their effectiveness in fostering financial inclusion depends on a balance between innovation, governance, and accessibility.
Peer-to-peer lending; financial inclusion; fintech; digital finance; credit access; borrower demographics; SME financing; logistic regression; loan defaults; financial technology innovation
Big Data Analytics and Its Role in Financial Risk Management
The increasing complexity of global financial systems and the exponential growth of data have positioned Big Data Analytics (BDA) as a transformative tool in financial risk management. This study examines how BDA enhances the identification, prediction, and mitigation of diverse financial risks, including credit, market, operational, and systemic risks. Using a mixed-method approach, the research integrates quantitative analysis of credit risk datasets, fraud detection models, and volatility forecasts with qualitative insights from case studies of financial institutions and fintech firms adopting big data frameworks. The results show that BDA significantly improves predictive accuracy in credit default modeling, enhances the detection of fraudulent transactions, and strengthens stress testing through real-time integration of structured and unstructured data. Moreover, systemic risk indicators derived from big data network analysis demonstrate the potential of BDA to map contagion effects and provide early warnings to regulators and policymakers. However, the findings also highlight challenges related to algorithmic transparency, interpretability, data privacy, and disproportionate compliance costs faced by smaller firms. The discussion emphasizes that while BDA offers substantial benefits, its long-term impact will depend on robust governance frameworks, ethical safeguards, and regulatory alignment. Overall, the study concludes that BDA represents not only a technological advancement but also a paradigm shift in financial risk management, redefining how risks are understood and managed across the financial ecosystem.
Big Data Analytics; financial risk management; credit risk; market volatility; systemic risk; operational risk; fraud detection; predictive modeling; stress testing; financial stability
The Effect of Robo-Advisors on Traditional Investment Advisory Services
The rapid rise of robo-advisors has disrupted the investment advisory landscape by offering low-cost, algorithm-driven financial advice that challenges the dominance of traditional human advisors. This study investigates the effects of robo-advisors on traditional advisory services using a mixed-method approach that combines quantitative analysis of adoption rates, cost structures, and performance metrics with qualitative evidence from case studies and client surveys. The results reveal that robo-advisors provide competitive portfolio performance and cost efficiency, driving widespread adoption among younger, digitally oriented investors. However, limitations in personalization, trust-building, and behavioral coaching continue to reinforce the value of human advisors, particularly for complex financial planning and wealth management. Tables and figures highlight that while robo-advisors expand inclusivity and democratize access to financial advice, human advisors remain indispensable for long-term client retention and high-value advisory services. Importantly, the findings show that hybrid advisory models, where human advisors leverage algorithmic tools, are becoming the dominant paradigm, blending the efficiency of automation with the relational depth of personalized service. The study concludes that robo-advisors are not substitutes but catalysts for structural transformation, compelling traditional advisors to adapt their roles within an increasingly digital and regulated environment. By fostering coexistence between technology and human expertise, the financial advisory industry can achieve greater accessibility, efficiency, and resilience, ensuring that innovation enhances rather than undermines trust in investment services.
Robo-advisors; traditional investment advisory; fintech; hybrid financial advice; cost efficiency; portfolio performance; risk-adjusted returns; investor trust; client retention; financial inclusion
CRISPR-Based Gene Editing in Disease Treatment: Ethical and Practical Challenges
CRISPR-based gene editing has rapidly emerged as a transformative technology in modern medicine, offering unprecedented precision in correcting genetic mutations and treating complex diseases. This study employed a mixed-methods framework integrating experimental analysis, computational modeling, and ethical inquiry to evaluate both the therapeutic potential and the challenges of CRISPR applications in disease treatment. Experimental results demonstrated marked improvements in editing efficiency and reductions in off-target events through optimized delivery systems and engineered Cas variants, while computational modeling provided predictive insights into repair pathway dynamics and mutation probabilities. However, persistent variability across trials highlights the continued technical limitations that must be addressed before widespread clinical translation is possible. Complementing these findings, a qualitative review of ethical and policy frameworks revealed broad support for somatic editing but strong global resistance to germline modification due to intergenerational risks, consent concerns, and potential exacerbation of social inequities. The study further identified distributive justice and equitable access as central challenges, with evidence suggesting that CRISPR therapies risk widening global health disparities without deliberate governance measures. Taken together, the results underscore that CRISPR’s clinical promise is inseparable from its ethical and regulatory context. Achieving safe and equitable implementation will require not only scientific innovation but also anticipatory governance, international collaboration, and inclusive stakeholder engagement. This integrated approach will be essential to ensure that CRISPR becomes a tool for collective health benefit rather than a source of new medical and social divisions.
CRISPR-Cas9, gene therapy, genome editing, disease treatment, ethical challenges, germline editing, somatic editing, off-target effects
Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
Electrochemical Pathways for Carbon Dioxide Reduction
To enhance efficiency and selectivity to value added products, we examine electrochemical processes by which carbon dioxide is reduced. A mixed methods approach was adopted and both qualitative structural characterization using SEM and TEM with quantitative electrochemical tools such as controlled- potential electrolysis and cyclic voltammetry were employed. Surface-functionalized and nanoporous catalysts were tested in CO 2 -saturated electrolytes, and the product distributions were determined by nuclear magnetic resonance spectroscopy and gas chromatography. This evidence demonstrates that surface architecture and electrode composition play a significant role in influencing the reduction outcomes, with the catalysts that have been optimized doing both better in overpotential reduction and determining higher Faradaic efficiencies. All products were analyzed according to the amounts formed, namely, methane, ethylene, carbon monoxide, and formic acid, and clear trends with catalyst design in the aspect of product specificity were observed. As far as statistical analysis of these results is concerned the significance and reliability of the results were proved. This work highlights the potential of electrochemical CO 2 reduction as a potential transformative technology of carbon recycling and the production of renewable fuels by combining mechanistic knowledge with experimental findings. The created process can be an excellent base of the future research, which canaim at the more global aspects of climate change mitigation and sustainable energy.
Electrochemical reduction, carbon dioxide, catalysts, Faradaic efficiency, renewable fuels, sustainability
Supramolecular Chemistry and Its Applications in Smart Materials
Supramolecular chemistry also has become a vital activity in the augmentation of the design and functioning of intelligent materials with a center and emphasis in non-covalent interactions and dynamic assembling points. This was demonstrated where a methodology strategy was used in this study to combine qualitative analysis of molecular recognition processes, in conjunction with quantitative experimental analysis in order to study the synthesis, characterisation limitations and performance of supramolecular systems. Covalent bonds were eliminated in favor of hydrogen bonding, host-guest, metal-ligand coordination, and 1-stacking to combine building blocks. Subsequently, the AFM, SAXS, DLS, UV-Vis and fluorescence spectroscopy, AFM, and SEM techniques were used to carefully analyze them. Thermodynamic analyses revealed both negative Gibbs free energy (3G) values as reliable indicators of spontaneous assembly, and kinetic measurements demonstrated the reversibility and responsiveness of these materials to a variety of environmental controls. Functional characterization indicated a high level of responsiveness in thermal, mechanical and electrical applications reflecting the possibilities of supramolecular systems to be deployed in sensors, responsive coatings, actuators and energy storage applications. The integration of synthesis, characterisation and functional evaluation within a single reproducible workflow (Fig. 1) suggests the effectiveness of supramolecular chemistry to come up with varied, dynamic and sustainable smart materials. Overall, these findings demonstrate that supramolecular chemistry can significantly contribute to the theoretical understanding of non-covalent interactions, as well as provide an effective and highly scalable means of creating smart material systems which are able to adapt dynamically to variability in the external environment.
Supramolecular chemistry, smart materials, molecular recognition, non-covalent interactions, self-assembly, functional responsiveness
Advances in Immunotherapy for Autoimmune and Inflammatory Disorders
Autoimmune and inflammatory disorders represent a significant global health burden, characterized by aberrant immune activation, chronic inflammation, and progressive tissue damage. Conventional therapies, including corticosteroids and disease-modifying antirheumatic drugs, often provide partial symptom relief but are limited by toxicity, incomplete efficacy, and high relapse rates. In recent years, immunotherapy has emerged as a transformative approach by selectively targeting immune pathways implicated in disease pathogenesis. This study synthesized evidence from recent clinical trials and real-world outcomes to evaluate the therapeutic advances of immunotherapy in autoimmune and inflammatory disorders between 2019 and 2022. Quantitative analyses of remission rates, relapse frequency, biomarker modulation, and safety profiles were combined with qualitative assessments of patient-reported outcomes and expert reviews. Results, presented in nine comprehensive tables and twelve figures, reveal that monoclonal antibodies, cytokine inhibitors, checkpoint modulators, CAR-T cell therapy, and JAK inhibitors have significantly enhanced disease control and quality of life compared to conventional treatments. Biomarker-guided stratification and multi-omics profiling have further enabled precision medicine approaches, aligning therapy with individual immune signatures. Despite these advances, the study also identified challenges, including adverse events such as infections and cytokine release syndrome, high treatment costs, and uncertainties regarding long-term safety. Economic analyses highlighted the importance of balancing innovation with accessibility, while relapse data reinforced the necessity of long-term monitoring. Collectively, the findings underscore that immunotherapy has redefined the management of autoimmune and inflammatory disorders by achieving durable remission and improving patient outcomes. Future research should focus on integrating biomarker-driven personalization, enhancing cost-effectiveness, and ensuring sustained safety, thereby consolidating the role of immunotherapy as a cornerstone in precision medicine for autoimmune disease care.
Immunotherapy, autoimmune disorders, inflammatory diseases, monoclonal antibodies, cytokine inhibitors, checkpoint inhibitors, CAR-T cells, JAK inhibitors, precision medicine, biomarkers
The Impact of Environmental Pollutants on Human Reproductive Health and Fertility Decline
This study investigates the impact of environmental pollutants on human reproductive health and the observed global decline in fertility rates. Using a mixed-methods approach, quantitative data were drawn from clinical records, environmental monitoring reports, and assisted reproductive technology (ART) outcomes, while qualitative insights were obtained from semi-structured interviews with patients, clinicians, and environmental experts. The statistical analyses revealed strong associations between exposure to pollutants—including endocrine-disrupting chemicals, heavy metals, particulate matter, and persistent organic pollutants—and adverse reproductive outcomes such as reduced sperm counts, impaired testicular function, elevated infertility rates, and decreased fertility success. Results from regression modeling demonstrated that higher pollutant exposure levels consistently correlated with lower fertility rates, with some pollutants showing infertility increases exceeding 25%. Complementing these findings, qualitative evidence underscored widespread concerns over pollutant exposure, highlighting lived experiences of fertility struggles and limited awareness of environmental risk factors. Collectively, the evidence confirms that pollutant exposure exerts significant negative effects on reproductive health through mechanisms of endocrine disruption, oxidative stress, and epigenetic modification. The study concludes that urgent interventions—including stricter regulatory policies, expanded public health education, and continued interdisciplinary research—are essential to safeguard reproductive health and ensure the sustainability of future populations.
environmental pollutants, fertility decline, endocrine disruptors, sperm quality, reproductive health, infertility
Global Health Inequalities: Structural Barriers to Accessing Affordable Healthcare in Low-Income Countries
This study explores the structural barriers to accessing affordable healthcare in low-income countries (LICs), emphasizing the role of economic, geographical, cultural, and political factors in healthcare inequality. By analyzing a diverse set of healthcare indicators, including healthcare access, infant mortality rates, life expectancy, and GDP per capita, the study identifies key determinants that contribute to disparities in healthcare outcomes. Results show that LICs, despite global health advancements, continue to face significant healthcare access challenges due to insufficient funding, inadequate infrastructure, and healthcare workforce shortages. Geographical barriers, particularly for rural populations, further exacerbate these disparities. The study also highlights the influence of political instability, which disrupts healthcare delivery, and the cultural and social factors that prevent marginalized communities from seeking care. The analysis reveals that while international aid has provided some relief, sustainable improvements depend on strengthening local healthcare systems and improving governance. The findings underscore the need for multifaceted policy reforms and investments that address both the economic and social determinants of health to reduce health inequalities in LICs. This research contributes to the understanding of health disparities and provides insights into policy recommendations aimed at improving healthcare access and outcomes in low-income regions.
Healthcare access, health inequality, low-income countries, healthcare infrastructure, political instability, health policy
Visual Narratives of Climate Change in Contemporary Art Exhibitions
Through a mixed-methods research design, one combining the qualitative thematic and statistical analysis, the study explores the role of contemporary art exhibitions in the representation and expression of climate change. Sixty global exhibitions were used to collect the data between 2015 and 2024. This data consisted of curatorial writings, visual records, visitor statistics and viewer surveys. The findings reported that the biodiversity/forest preservation/glacial retreat-related displays were much better engaged than the more particular displays regarding ecology. Regression modelling revealed that the engagement of the visitors also depended on the audience size in addition to genre diversification and curatorial approach. There was a lot of popularity in exhibitions which were interactive and that told stories, getting people interested was important. Regional comparison was also used to highlight cultural differences, as European shows were more centered towards ecology issues whereas North American shows were more concerned with air pollution issues and industry impact. These tendencies were supported by survey results of the audience, who believed that exhibitions about which there are many stories are more effective among younger people. The synthesis of both qualitative and quantitative findings produced the evidence that tactical methods employed by artists that merge science with human-oriented story editorializing come a great way in enhancing the communication of climate change. The research concludes that modern art exhibits are excellent methods of informing the people about the environmental problems and engaging them in the process as it relates the science argument to a practical behavior. Such insights can contribute to the body of cultural sustainability work because they aptly demonstrate how art may mobilize audiences into ecological consciousness in the Anthropocene.
climate change, contemporary art, exhibitions, visual narratives, audience engagement, sustainability
Feminist Approaches to Interpreting Renaissance Art
The study focuses on Renaissance art through feminist sensibilities, through a combination of quantitative analysis and qualitative representation of how gender is represented through the visual rhetoric, symbolism, reliance upon patronage, and reception itself. The available data were collected in archival records, the museum catalogue, and online repositories, including works that do not have the same iconic status as those that received much greater attention but also less-known contributions of female artists. Software-aided quantitative analysis showed the female characters to be under-represented compared to their male counterparts, but theme types like Madonna and mythical allegories had characteristic female-representative undertones. Patronage studies revealed that commissioners were mainly men yet women patrons played a huge part in furthering feminist ideals. Seasoned regressive modelling of past review revealed that there was an emerging trend in positive coverage of the art with female focus in the early years of the sixteenth century. Qualitative analysis also added context to these findings, demonstrating how symbols such as the Virgin Mary, Eve, gallery allegorical figures and others not only supported the cultural constructions of female and embodied resilience and legitimate moral authority but they also challenged national time and manipulated national authenticity. A comparative study between female gardens and female painters showed a clear institutional marginalisation of female work several major contributions are either forgotten or under-represented within museum displays. Increasing credit was given to the contribution of women and reassessment of feminist themes in the Renaissance art, which were recorded in feminist publications and modern exhibitions. The methods of quantification and thematic analysis support a reworking of the Renaissance visual culture that is feminist-informed and advanced. These results indicate that feminist approaches not only expose unevenness of the past but also provide fundamental guidelines that augment knowledge of cultural identity and representation in the early modern Europe.
Feminist art history, Renaissance art, gender representation, symbolism, patronage, reinterpretation
The Impact of Digital Technology on Traditional Painting Practices
The incorporation of digital technology in traditional painting practices has brought great change in production of painting works, economy and culture. In this research study, the researcher applied a mixed-methods experimental design combining quantitative surveys through questionnaires of 200 participants in the art sector and qualitative interviews with 25 participants in the art sector also experimental trials of similar evaluations of digital and conventional outputs. The findings revealed that artists are embracing a greater use of hybrid forms and the results indicated that there are increases in the expended time, the cost of materials and the sources of income to be more digital in nature. It was seen that participation in online was much more as compared to participation at physical exhibitions. This demonstrates that things can be seen much more on digital platforms. The statistical analysis showed that there were high differences in frequencies of partnership and training path-ways, and the qualitative results highlighted themes of originality, affordability, and in innovative trial. Experimental comparisons revealed that there was not much difference between creativity perception scores of traditional and digital outputs. This is contrary to the perception that digital is not good. In its findings the digital technology is altering practices of people painting by the creation of new job prospects, the expanded networks of collaboration partnerships and modifications of the perceptions of innovation as people continue to preserve the ideological worth of the traditional practice. This paper shows that the hybrid nature of painting will emerge as a result of the intersection of tactile and digital innovations in the future.
Digital painting, traditional art, hybrid practices, creativity perception, audience engagement, artistic economy
Memory and Commemoration: The Politics of Historical Monuments
This study will focus on the politics of historic monuments as places of historical memory, identity and violence. The study used a mixed-methods study that involved both a quantitative statistical modelling technique and a qualitative thematic-analysis one to come up with patterns in distributions, symbolism, funding, gender representation and acknowledging of monuments. Findings reveal the highest number of political and war memorials are in metropolitan centres, and cultural and religious monuments in the rural. Historical studies indicate that construction growths occur after periods of warfare whereas gender studies show female figures are still not keenly represented in commemorative statues. Quantitative models demonstrate that the inflow of government funding is greatly connected to higher attendance rates. On one hand, the initiatives led by the NGOs diversify symbolic content even with limited resources. Indeed, international recognition, particularly by UNESCO, was found to have significant impact on investments in conservation and publicity on a global scale. The findings are examined by qualitative discourse study as well that shows that monuments can simultaneously propagate major narratives and serve as disputed spaces of minority discourse. The work also discovered that de-commemoration, that is, the removal and reinterpretation of monuments, does not erase the consideration of justice and collective identity, but rather continues the discourse. The combination of statistical data with thematic categorization demonstrates how monuments become the meeting point of politics, culture, and even ethics, which allow shaping memoryscapes on a national and global level. On the whole, the results make a great contribution to the heritage and memory research as they show that monuments are not fixed symbols but dynamic symbols of collective history constantly under redefinition.
Monuments, collective memory, commemoration politics, heritage studies, cultural identity, public engagement
Artificial Intelligence in Enhancing Fraud Detection in Banking Transactions
Fraudulent activities in banking transactions pose significant threats to financial institutions and customers, with traditional rule-based detection systems proving insufficient against increasingly sophisticated fraud schemes. This study investigates the role of artificial intelligence (AI) in enhancing fraud detection through a mixed-methods experimental design combining quantitative evaluation of supervised, unsupervised, and hybrid models with qualitative assessments of explainability and regulatory compliance. Using a dataset of over five million transactions, models including Random Forest, Gradient Boosting, Deep Neural Networks, Autoencoders, and Graph Neural Networks were applied. Results reveal that AI-based models achieved superior accuracy, recall, and AUC values compared to conventional systems, with ensemble and hybrid approaches effectively reducing false positives. Federated learning demonstrated promise in enabling secure cross-bank fraud detection, while explainable AI frameworks such as SHAP ensured transparency and regulatory alignment. The findings highlight not only the technological advantages of AI in fraud detection but also its strategic implications in reducing losses, improving customer experience, and enhancing institutional trust. This research provides both academic and practical contributions, underscoring AI as a pivotal tool in safeguarding the integrity of modern banking transactions.
Artificial Intelligence, Fraud Detection, Banking Transactions, Machine Learning, Explainable AI, Federated Learning
Generational Differences in Online Shopping Behavior
This paper examines the issue of generational differences in the online shopping behavior using a mixed-method research methodology including quantitative survey (n = 1,200) and qualitative interview (n = 40), as a research methodology. The study is based on the four generational groups, such as the Baby Boomers, Generation X, Millennials, and Generation Z, and the motivation and perception of trust, preferences on payment and loyalty. The statistical modeling was conducted on significant generational differences, with both the regression analysis and structural equation modeling; thematic analysis were also utilized to gain more information regarding the consumer perceptions and barriers. Findings show that younger generations (Millennials and Gen Z) are mostly being motivated by hedonic values, social media, and tech innovations like mobile wallets and AI-enabled personalisation. Conversely, older groups (Baby Boomers and Gen X) have greater utilitarian value, increased trust issues, and loyalty to platforms they have been using. The research also defines sustainability as a major motivator to the younger generation of customers and cost and convenience as a priority among the older generations. These results generalize theoretical models like Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), which emphasize on the mediating processes of trust and social influence on the online shopping behavior among the age groups. Practical implications include the fact that e-commerce companies have to adopt generationally segmented strategy and strike a balance between innovation and security so that they can cater to heterogeneous consumer expectations. The research can fit into the body of consumer behavior, digital adoption, and marketing strategy studies and be of value to scholars, practitioners, and policymakers working in a more digitized market.
Generational differences, online shopping behavior, e-commerce adoption, consumer trust, digital retailing, sustainability
Neuromarketing Insights into Consumer Decision Processes
Neuromarketing is now an interdisciplinary science, which finds the unconscious logic behind consumer decisions by synthesizing neuroscience, psychology and marketing. The mixed-method design that the current study adopted consisted of a combination of EEG, fMRI, eye-tracking, skin conductance and heart rate variability along with qualitative focus groups and the thematic analysis. Data was analyzed using structural equation modelling with 300 participants to predict the forecastive value of attention, emotional arousal and memory encoding on the purchase intention. Results revealed that emotionally evoking stimuli significantly enhanced physiological arousal, EEG asymmetry, and fMRI activations within the parts of the brain that were related to reward, hence anticipating a better purchase intention. The young consumers had high brand recall and gender difference existed according to feelings and cognitive processing. Such a mixture of neurophysiological and qualitative data presented a comprehensive approach to the process of knowledge about consumer behavior grounded on the study other than self-reports. The findings are informative to the consumer neuroscience theory, demonstrate the importance of the subconscious processes in the decision-making process and provide the actual world implications to the marketers in the context of developing an emotionally effective, and the morally responsible, campaign. In this study, it was determined that neuromarketing is a very predictive, ethically sound, and power-filled path to understand consumer choice during the technological age.
Neuromarketing, Consumer Decision-Making, EEG, fMRI, Emotional Arousal, Purchase Intention
The Impact of Early Childhood Education on Lifelong Learning
This study examined the long-term effects of early childhood education (ECE) on lifelong learning outcomes using a mixed-method experimental design that combined quantitative analyses of developmental and educational indicators with qualitative insights from participants, parents, and educators. Quantitative findings across nine datasets demonstrated that children exposed to ECE programs achieved significantly higher academic readiness scores, literacy performance at age ten, primary and secondary school completion rates, and college enrollment compared to peers without ECE exposure. Employment outcomes at age 25 and health-related lifestyle indicators were also notably stronger among ECE participants. Regression models confirmed that program quality, duration, and socio-economic background were significant predictors of a higher lifelong learning index. Complementary qualitative findings revealed that ECE instilled motivation, adaptability, and socio-emotional resilience, which supported continuous learning throughout adulthood. Twelve figures visualized these results, including line, bar, pie, scatter, hybrid, and radar plots, which highlighted correlations between ECE exposure and positive lifelong outcomes. The findings underscore that high-quality early childhood education not only enhances academic achievement but also fosters holistic development, reduces inequality, and strengthens long-term social and economic well-being.
early childhood education, lifelong learning, academic readiness, educational attainment, employment outcomes, social equity
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Special Education Policies and Their Effect on Student Integration
This study investigated the impact of special education policies on student integration using a mixed-method experimental design that combined quantitative assessments with qualitative insights. A sample of students with disabilities and their peers in mainstream classrooms was divided into intervention and control groups, with the intervention group exposed to targeted inclusive policies such as individualized education plans, teacher training, and enhanced resource provision. Quantitative analysis revealed that schools implementing these policies demonstrated significantly higher student integration scores, with improvements evident in peer acceptance, classroom participation, teacher support, and organizational trust. Tables 1–9 highlighted consistent gains across policy implementation, disability accommodation, collaborative learning, and school culture indicators. Figures 2–13 visually reinforced these findings, showing steady increases in wellbeing, participation, and retention rates. Qualitative interviews with teachers, students, and parents further emphasized that transparent communication, resource allocation, and family engagement were essential drivers of successful integration. The results confirm that policies designed with a focus on equity, access, and support not only improve academic outcomes but also enhance social belonging and overall student wellbeing. This research concludes that special education policies serve as powerful instruments of systemic change, with their effectiveness determined by teacher preparedness, adequate resources, and community involvement. The study provides valuable implications for policymakers and practitioners, underscoring the need for context-sensitive, well-funded, and collaborative approaches to inclusive education.
special education policies, student integration, inclusive education, teacher support, peer acceptance, student wellbeing