Nova Integrata: Journal of Multidisciplinary Studies
type: channel
description: 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.
publisher: CODEX ACADEMIA (SMC-PRIVATE) LIMITED
language: en
publication name: Nova Integrata: Journal of Multidisciplinary Studies
issn: 3106-1338
items: DIGITAL STORYTELLING FOR ENHANCING VISITOR COMPREHENSION OF CULTURAL ARTIFACTS IN MUSEUMS
items: OSMOTIC POWER GENERATION: MEMBRANE DESIGN, EFFICIENCY, AND DEPLOYMENT BARRIERS
items: MISINFORMATION RESILIENCE AND DEMOCRATIC PARTICIPATION IN THE ERA OF GENERATIVE AI: A COMPARATIVE SOCIAL SCIENCE STUDY
items: MULTIMODAL MACHINE LEARNING FOR DIFFERENTIAL DIAGNOSIS OF CHRONIC OTALGIA
items: AI-ASSISTED OPTIMIZATION OF ANALGESIA AND SEDATION PROTOCOLS IN MECHANICALLY VENTILATED ICU PATIENTS WITH SEVERE PAIN
items: Advancements in Structural Engineering: Designing Earthquake-Resilient Buildings for Urban Environments
items: The Future of Autonomous Vehicles: Analyzing the Technological and Ethical Challenges of Self-Driving Cars
items: A Study on the Impact of Nanotechnology in Drug Delivery Systems: Innovations in Cancer Treatment
items: The Development of Biodegradable Plastics: Challenges and Opportunities for Sustainable Packaging Solutions
items: Exploring the Role of Artificial Intelligence in Predicting Climate Change Patterns and Mitigating Environmental Disasters
items: Examining the Role of Museums in Preserving Cultural Heritage and Promoting National Identity
items: The Representation of Race and Ethnicity in Modern Cinema: A Critical Study of Hollywood's Racial Dynamics
items: The Ethics of Artificial Intelligence in Literature: Analyzing the Portrayal of AI in Science Fiction
items: AI-Driven Genomic Analysis for Identifying Novel Biomarkers in Precision Medicine
items: The Role of Machine Learning in Remote Patient Monitoring and Telemedicine for Chronic Disease Management
items: AI Applications in Improving Public Service Delivery Efficiency
items: AI-Driven Analysis of Cross-Cultural Communication Patterns
items: The Impact of Peer-to-Peer Lending Platforms on Financial Inclusion
items: Big Data Analytics and Its Role in Financial Risk Management
items: The Effect of Robo-Advisors on Traditional Investment Advisory Services
items: CRISPR-Based Gene Editing in Disease Treatment: Ethical and Practical Challenges
items: Electrochemical Pathways for Carbon Dioxide Reduction
items: Supramolecular Chemistry and Its Applications in Smart Materials
items: Advances in Immunotherapy for Autoimmune and Inflammatory Disorders
items: The Impact of Environmental Pollutants on Human Reproductive Health and Fertility Decline
items: Global Health Inequalities: Structural Barriers to Accessing Affordable Healthcare in Low-Income Countries
items: Visual Narratives of Climate Change in Contemporary Art Exhibitions
items: Feminist Approaches to Interpreting Renaissance Art
items: The Impact of Digital Technology on Traditional Painting Practices
items: Memory and Commemoration: The Politics of Historical Monuments

DIGITAL STORYTELLING FOR ENHANCING VISITOR COMPREHENSION OF CULTURAL ARTIFACTS IN MUSEUMS
type: item
description: The study presented in this paper looks at the use of digital storytelling to enhance the understanding of the visitors on cultural artefacts in museums. As is common in museums, short labels, static descriptions, and limited contextual information are often used to describe the objects on display that might restrict visitors' ability to make connections between artifacts and their historical, cultural, and social contexts. Digital storytelling is a more effective way to communicate meaningfully and accessibly about artifacts through story and images, audio, video, animation and interactive media. It examines how the interpretation of objects in the museum can be enhanced through the use of story based Digital Interpretation in regard to the place of their origin, use, symbolism and cultural value. It also considers the potential effect technology can have on engaging emotions, memory retention and satisfaction of learning for a variety of audiences. The findings reveal that digital storytelling has the potential to affect the learning of the viewer at the museum and to demystify and personalize artefacts. But the real challenge is in designing content that is effective, culturally appropriate, technically accessible and in the right balance with the authenticity of physical objects in the museum. The paper recommends digital storytelling as a good way to enhance museum education and experience.
subject: Social Sciences and Humanities
  topic: section
subject: Heritage Interpretation
  topic: keywords
subject: Visitor Engagement;
  topic: keywords
subject: Museum Learning
  topic: keywords
subject: Cultural Artifacts
  topic: keywords
subject: Digital Storytelling
  topic: keywords
creator: Danish Farooq
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 16
ending page: 31
doi: 10.66382/nijms1.77

OSMOTIC POWER GENERATION: MEMBRANE DESIGN, EFFICIENCY, AND DEPLOYMENT BARRIERS
type: item
description: Osmotic power systems have emerged as a promising low-carbon energy technology capable of converting salinity-gradient energy into usable electricity. This paper examines the role of membrane design, system efficiency, and deployment barriers in determining the practical viability of osmotic power generation. The study focuses on pressure-retarded osmosis and related membrane-based configurations, where freshwater and saline water interactions generate pressure or ionic movement that can be converted into electrical power. The results indicate that membrane performance remains the most critical factor influencing energy output, particularly in terms of water flux, salt rejection, fouling resistance, and long-term mechanical stability. Improved membrane selectivity and reduced internal concentration polarization were found to enhance power density and operational efficiency. However, large-scale implementation is still constrained by high membrane fabrication costs, pretreatment requirements, biofouling, limited field validation, and site-specific availability of suitable freshwater–saltwater gradients. The findings suggest that osmotic power can support future renewable energy portfolios, especially in coastal regions, wastewater treatment plants, desalination facilities, and industrial brine management systems. Overall, the study highlights that osmotic power is technically promising but requires further advances in membrane materials, module design, cost reduction, and policy support before it can become commercially competitive with mature renewable energy technologies.
subject: Engineering And Technology
  topic: section
subject: Osmotic power; salinity-gradient energy; pressure-retarded osmosis; membrane design; low-carbon energy
  topic: keywords
creator: Muhammad Salman Iqbal
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 13
doi: 10.66382/nijms1.76

MISINFORMATION RESILIENCE AND DEMOCRATIC PARTICIPATION IN THE ERA OF GENERATIVE AI: A COMPARATIVE SOCIAL SCIENCE STUDY
type: item
description: The rapid expansion of generative artificial intelligence has transformed the production, circulation, and personalization of political information, creating new challenges for democratic participation and public trust. This study examines misinformation resilience and democratic participation in the era of generative AI through a comparative social science perspective. The paper focuses on how citizens’ digital literacy, trust in AI-generated content, platform exposure, fact-checking behavior, and institutional confidence shape their ability to identify and resist misleading political information. The results indicate that misinformation resilience is strongly associated with higher levels of digital media literacy, critical verification habits, and confidence in democratic institutions. Participants with stronger fact-checking behavior and lower uncritical trust in AI-generated content demonstrated greater ability to recognize manipulated narratives and were more likely to maintain constructive political engagement. The comparative findings further suggest that younger respondents showed higher exposure to generative AI content, while older respondents displayed more cautious attitudes toward AI-mediated political information. Social media platforms were identified as the dominant channel through which AI-driven misinformation circulates, highlighting the need for platform accountability and public education. Overall, the study shows that generative AI does not only intensify misinformation risks but also creates an urgent need to strengthen civic resilience, digital literacy, and democratic safeguards. The findings contribute to ongoing debates on AI governance, political communication, and democratic sustainability in digitally mediated societies.
subject: Social Sciences and Humanities
  topic: section
subject: Misinformation resilience, generative artificial intelligence, democratic participation, digital literacy, political communication
  topic: keywords
creator: Muhammad Ali Raza
creator: Syed Hamza Jafri
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 15
doi: 10.66382/nijms1.75

MULTIMODAL MACHINE LEARNING FOR DIFFERENTIAL DIAGNOSIS OF CHRONIC OTALGIA
type: item
description: Chronic otalgia is a persistent ear pain condition that may arise from primary otologic disorders or referred pain from dental, temporomandibular, neurological, cervical, or head and neck pathologies. Because its causes are often overlapping and clinically complex, accurate differential diagnosis can be challenging when audiological, imaging, and clinical findings are interpreted separately. This study proposes a chronic multimodal machine learning framework for the differential diagnosis of chronic otalgia using integrated audiological, radiological, and clinical features. Audiological variables may include pure-tone audiometry, tympanometry, speech discrimination scores, and hearing asymmetry, while imaging features may be extracted from computed tomography or magnetic resonance imaging reports. Clinical variables may include pain duration, laterality, otoscopic findings, associated tinnitus, vertigo, dental history, temporomandibular joint symptoms, neurological signs, and prior treatment response. By combining these heterogeneous data sources, the proposed model aims to identify diagnostic patterns that may be missed through single-modality assessment. The multimodal approach is expected to improve classification performance, support early recognition of referred otalgia, and assist clinicians in prioritizing further investigations. The study highlights the potential of machine learning as a clinical decision-support tool for chronic otalgia, particularly in cases where conventional evaluation produces uncertain or overlapping diagnostic impressions. Overall, the framework may contribute to more accurate diagnosis, reduced diagnostic delay, and personalized management of patients presenting with chronic ear pain.
subject: Health and Life Sciences
  topic: section
subject: Chronic otalgia; multimodal machine learning; differential diagnosis; audiological features; medical imaging; clinical decision support
  topic: keywords
creator: Muhammad Talha Siddiqui
creator: Ayesha Imran
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 30
ending page: 49
doi: 10.66382/nijms1.74

AI-ASSISTED OPTIMIZATION OF ANALGESIA AND SEDATION PROTOCOLS IN MECHANICALLY VENTILATED ICU PATIENTS WITH SEVERE PAIN
type: item
description: In the intensive care unit, in patients under mechanical ventilation, the use of artificial intelligence is becoming more and more present in the management of analgesia and sedation, especially in patients with severe pain and complex physiological instability. This paper discusses the potential utility of AI optimization, focusing on the use of reinforcement learning, deep learning, multimodal monitoring, and closed loop decision support systems for personalized drug titration. Traditional sedation/analgesia management is based on intermittent clinical evaluation and clinician-driven dosage that can lead to oversedation, undertreatment of pain, hypotension, delirium, lengthened ventilation and later recovery. An AI system, on the other hand, can consider real-time physiological data, EHRs, pain levels, ventilator metrics, and pharmacokinetic-pharmacodynamic models to make patient-specific recommendations for dosing. The study not only identifies the advantages of reinforcement learning agents, conservative Q-learning, actor-critic architectures, and multi-agent learning frameworks in enhancing the accuracy of sedative and analgesic administration but also addresses measures taken to ensure safety standards consistent with expert practices. The paper also covers the pillars of explainability, clinician trust, ethical deployment, multicenter validation and regulatory readiness before these technologies can be widely adopted into the bed-side workflow of the intensive care unit. In conclusion, AI tools for optimizing analgesia and sedation in critical care are a promising frontier for improving patient safety, personalization, and efficiency, but they will require more extensive and rigorous clinical trials, clear design of the models, and a human-centered approach to implementation.
subject: Health and Life Sciences
  topic: section
subject: Artificial intelligence; Reinforcement learning; Analgesia optimization; ICU sedation; Mechanical ventilation
  topic: keywords
creator: Syed Fahad Raza
creator: Ayesha Naveed
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 29
doi: 10.66382/nijms1.73

Advancements in Structural Engineering: Designing Earthquake-Resilient Buildings for Urban Environments
type: item
description: 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 barrie…
subject: Engineering And Technology
  topic: section
subject: Earthquake Engineering
  topic: keywords
subject: Seismic Resilience
  topic: keywords
subject: Base Isolation
  topic: keywords
subject: Structural Control
  topic: keywords
subject: Performance-Based Design
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subject: Urban Infrastructure
  topic: keywords
subject: Damage Reduction
  topic: keywords
creator: Kamran Ahmed
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 12
ending page: 26
doi: 10.66382/nijms2.71

The Future of Autonomous Vehicles: Analyzing the Technological and Ethical Challenges of Self-Driving Cars
type: item
description: 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 …
subject: Engineering And Technology
  topic: section
subject: Autonomous Vehicles
  topic: keywords
subject: Self-Driving Cars
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subject: Ethical Algorithms
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subject: Sensor Fusion
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subject: Cybersecurity
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subject: Transportation Policy
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subject: Artificial Intelligence
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creator: Usman Tariq
creator: Hira Mahmood
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 11
doi: 10.66382/nijms2.70

A Study on the Impact of Nanotechnology in Drug Delivery Systems: Innovations in Cancer Treatment
type: item
description: 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 tha…
subject: Natural and Applied Sciences
  topic: section
subject: Nanotechnology
  topic: keywords
subject: Drug Delivery Systems
  topic: keywords
subject: Cancer Therapy
  topic: keywords
subject: Nanomedicine
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subject: Targeted Therapy
  topic: keywords
subject: Nanoparticles
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subject: Translational Research
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creator: Faisal Mehmood
creator: Ayesha Rahman
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 32
ending page: 48
doi: 10.66382/nijms2.69

The Development of Biodegradable Plastics: Challenges and Opportunities for Sustainable Packaging Solutions
type: item
description: 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 biode…
subject: Natural and Applied Sciences
  topic: section
subject: Biodegradable Plastics
  topic: keywords
subject: Sustainable Packaging
  topic: keywords
subject: Polymer Science
  topic: keywords
subject: Circular Economy
  topic: keywords
subject: Environmental Impact
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subject: Material Science
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subject: Compostable Materials
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creator: Adeel Hussain
creator: Sadia Noor
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 16
ending page: 31
doi: 10.66382/nijms2.68

Exploring the Role of Artificial Intelligence in Predicting Climate Change Patterns and Mitigating Environmental Disasters
type: item
description: 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 …
subject: Natural and Applied Sciences
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Climate Prediction
  topic: keywords
subject: Disaster Mitigation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Environmental Modeling
  topic: keywords
subject: Extreme Weather
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subject: Resilience Planning
  topic: keywords
creator: Muhammad Ahsan Raza
creator: Sana Khalid
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 15
doi: 10.66382/nijms2.67

Examining the Role of Museums in Preserving Cultural Heritage and Promoting National Identity
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Museums
  topic: keywords
subject: Cultural Heritage
  topic: keywords
subject: National Identity
  topic: keywords
subject: Collective Memory
  topic: keywords
subject: Visitor Experience
  topic: keywords
subject: Heritage Preservation
  topic: keywords
subject: Museum Storytelling
  topic: keywords
creator: Farzana Iqbal
creator: Kamran Ali
rights: Copyright (c) 2025 Farzana Iqbal, Kamran Ali (Author) https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-12-31
publication date: 2025-12-31
starting page: 35
ending page: 51
doi: 10.66382/nijms2.66

The Representation of Race and Ethnicity in Modern Cinema: A Critical Study of Hollywood's Racial Dynamics
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Race And Ethnicity
  topic: keywords
subject: Hollywood Cinema
  topic: keywords
subject: Media Representation
  topic: keywords
subject: Critical Race Theory
  topic: keywords
subject: Film Studies
  topic: keywords
subject: Diversity And Inclusion
  topic: keywords
subject: Cultural Discourse
  topic: keywords
creator: Sana Rauf
creator: Arif Mehmood
rights: Copyright (c) 2025 Sana Rauf, Arif Mehmood (Author) https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-12-31
publication date: 2025-12-31
starting page: 18
ending page: 34
doi: 10.66382/nijms2.65

The Ethics of Artificial Intelligence in Literature: Analyzing the Portrayal of AI in Science Fiction
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Artificial Intelligence Ethics
  topic: keywords
subject: Science Fiction Literature
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Ethical Narratives
  topic: keywords
subject: Surveillance And Bias
  topic: keywords
subject: Human–AI Relations
  topic: keywords
subject: Digital Humanities
  topic: keywords
creator: Huma Siddiqui
creator: Danish Rahman
rights: Copyright (c) 2025 Huma Siddiqui, Danish Rahman (Author) https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 17
doi: 10.66382/nijms2.64

AI-Driven Genomic Analysis for Identifying Novel Biomarkers in Precision Medicine
type: item
description: 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.
subject: Health and Life Sciences
  topic: section
subject: Artificial intelligence, genomic analysis, biomarker discovery, precision medicine, multi-omics, personalized healthcare
  topic: keywords
creator: Saad Abdullah
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 17
ending page: 33

The Role of Machine Learning in Remote Patient Monitoring and Telemedicine for Chronic Disease Management
type: item
description: 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.
subject: Health and Life Sciences
  topic: section
subject: machine learning, telemedicine, remote patient monitoring, chronic disease, predictive healthcare, digital health
  topic: keywords
creator: Rida Naz
creator: Muska Hayat
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 16

AI Applications in Improving Public Service Delivery Efficiency
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Artificial Intelligence, Public Service Delivery, Efficiency, Governance, Automation, Transparency
  topic: keywords
creator: Asma Tariq
creator: Junaid Mehmood
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 19
ending page: 35

AI-Driven Analysis of Cross-Cultural Communication Patterns
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Artificial Intelligence, Cross-Cultural Communication, Machine Learning, Multimodal Analysis, Intercultural Studies, Communication Patterns
  topic: keywords
creator: Hira Jamal
creator: Owais Khan
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 18

The Impact of Peer-to-Peer Lending Platforms on Financial Inclusion
type: item
description: 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.
subject: Business And Finance
  topic: section
subject: Peer-to-peer lending; financial inclusion; fintech; digital finance; credit access; borrower demographics; SME financing; logistic regression; loan defaults; financial technology innovation
  topic: keywords
creator: Sana Iqbal
creator: Muneeb Khalid
creator: Saifullah
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 38
ending page: 57

Big Data Analytics and Its Role in Financial Risk Management
type: item
description: 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.
subject: Business And Finance
  topic: section
subject: Big Data Analytics; financial risk management; credit risk; market volatility; systemic risk; operational risk; fraud detection; predictive modeling; stress testing; financial stability
  topic: keywords
creator: Shehla Mazhar
creator: Zara Yousaf
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 20
ending page: 37

The Effect of Robo-Advisors on Traditional Investment Advisory Services
type: item
description: 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.
subject: Business And Finance
  topic: section
subject: Robo-advisors; traditional investment advisory; fintech; hybrid financial advice; cost efficiency; portfolio performance; risk-adjusted returns; investor trust; client retention; financial inclusion
  topic: keywords
creator: Aqsa Malik
creator: Hassan Raza
rights: Copyright (c) 2025 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 19

CRISPR-Based Gene Editing in Disease Treatment: Ethical and Practical Challenges
type: item
description: 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.
subject: Natural and Applied Sciences
  topic: section
subject: CRISPR-Cas9, gene therapy, genome editing, disease treatment, ethical challenges, germline editing, somatic editing, off-target effects
  topic: keywords
creator: Mahnoor Ali
creator: Hamza Tariq
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 44
ending page: 61

Electrochemical Pathways for Carbon Dioxide Reduction
type: item
description: 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.
subject: Natural and Applied Sciences
  topic: section
subject: Electrochemical reduction, carbon dioxide, catalysts, Faradaic efficiency, renewable fuels, sustainability
  topic: keywords
creator: Nida Hassan
creator: Salman Akhtar
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 24
ending page: 43

Supramolecular Chemistry and Its Applications in Smart Materials
type: item
description: 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.
subject: Natural and Applied Sciences
  topic: section
subject: Supramolecular chemistry, smart materials, molecular recognition, non-covalent interactions, self-assembly, functional responsiveness
  topic: keywords
creator: Farah Naz
creator: Adnan Rauf
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 23

Advances in Immunotherapy for Autoimmune and Inflammatory Disorders
type: item
description: 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 perso…
subject: Health and Life Sciences
  topic: section
subject: Immunotherapy, autoimmune disorders, inflammatory diseases, monoclonal antibodies, cytokine inhibitors, checkpoint inhibitors, CAR-T cells, JAK inhibitors, precision medicine, biomarkers
  topic: keywords
creator: Ranya Mohammed Elmagzoub
creator: Zafar Aleem Suchal
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 39
ending page: 61

The Impact of Environmental Pollutants on Human Reproductive Health and Fertility Decline
type: item
description: 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.
subject: Health and Life Sciences
  topic: section
subject: environmental pollutants, fertility decline, endocrine disruptors, sperm quality, reproductive health, infertility
  topic: keywords
creator: Rabia Nasir
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 19
ending page: 38

Global Health Inequalities: Structural Barriers to Accessing Affordable Healthcare in Low-Income Countries
type: item
description: 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.
subject: Health and Life Sciences
  topic: section
subject: Healthcare access, health inequality, low-income countries, healthcare infrastructure, political instability, health policy
  topic: keywords
creator: Hassan Yar Mahsood
creator: Abdul Ghaffar
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 18

Visual Narratives of Climate Change in Contemporary Art Exhibitions
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: climate change, contemporary art, exhibitions, visual narratives, audience engagement, sustainability
  topic: keywords
creator: Amina Rehman
creator: Zeeshan Ali
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 21
ending page: 39

Feminist Approaches to Interpreting Renaissance Art
type: item
description: 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 approa…
subject: Social Sciences and Humanities
  topic: section
subject: Feminist art history, Renaissance art, gender representation, symbolism, patronage, reinterpretation
  topic: keywords
creator: Sadia Khan
creator: Nimra Aftab
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 20

The Impact of Digital Technology on Traditional Painting Practices
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Digital painting, traditional art, hybrid practices, creativity perception, audience engagement, artistic economy
  topic: keywords
creator: Maria Salman
creator: Usman Qadir
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-06-30
publication date: 2024-06-30
starting page: 20
ending page: 36

Memory and Commemoration: The Politics of Historical Monuments
type: item
description: 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.
subject: Social Sciences and Humanities
  topic: section
subject: Monuments, collective memory, commemoration politics, heritage studies, cultural identity, public engagement
  topic: keywords
creator: Ayesha Riaz
creator: Bilal Ahmed
rights: Copyright (c) 2024 Nova Integrata: Journal of Multidisciplinary Studies https://creativecommons.org/licenses/by-nc/4.0/deed.en
date: 2024-06-30
publication date: 2024-06-30
starting page: 1
ending page: 19