Spectrum of Research and Reviews (SRR) is a triple-blind peer-reviewed academic journal published by THE LUMINARY LEARNING INSTITUTE (SMC-PRIVATE) LIMITED. The journal provides a platform for researchers, academics, and professionals to publish high-quality research across multiple disciplines. It emphasizes rigorous peer review, originality, and scholarly excellence. The journal is published semiannually. Scope The journal covers a multidisciplinary range of topics, including but not limited to: Physical and Life Sciences Medicine and Allied Health Sciences Clinical and Medical Sciences Environmental Science and Sustainability Health and Medical Sciences Business, Management, and Economics Interdisciplinary and Applied Research
THE LUMINARY LEARNING INSTITUTE (SMC-PRIVATE) LIMITED
ETHICAL AND REGULATORY CHALLENGES OF AI DEPLOYMENT IN AUTONOMOUS WEAPONS SYSTEMS
This paper analyses the considerations of ethics and the regulation challenges, as applied to the application of artificial intelligence in autonomous weapons systems, which will be conducted using a mixed-method research methodology. To evaluate the accuracy of AI judgment, the probability of committing an ethical violation, the accumulation of risk, or the effectiveness of the human control in different cases of autonomous interactions, quantitative simulations were conducted. The findings demonstrate that AI systems are ethical in most instances but the more autonomous and the less stable environment the less ethical they become. It was observed that ethical risk was increasing over time, and the principles of proportionality and discrimination were regularly violated in the fully independent situations. Graphic presentations of experimental results reveal that there is a declining level of transparency and also the capacity of people to overrule is reducing, which poses some serious questions about accountability. Qualitative regulatory review indicates that existing international legal systems are weak in alleviating these factual risks, as they do not specify enforceable methods to use to enforce real-time compliance with ethics and post-deployment responsibility. The combination of the results highlights a growing gap between technological capability and ethical control, which reveals the urgent need to reinforce international controls, establish human surveillance as a mandatory requirement, and enforce ethical constraints with proven constraints in AI-controlled autonomous weapon systems.
Artificial Intelligence, Autonomous Weapons Systems, Ethical Governance, International Humanitarian Law, AI Regulation, Military AI
Copyright (c) 2025 Muneeba, Zia Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
INVESTIGATING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON JOB AUTOMATION AND EMPLOYMENT IN THE GLOBAL WORKFORCE
<span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The rapid diffusion of artificial intelligence has intensified global debate regarding its implications for employment, job displacement, and workforce transformation. This study investigates the multifaceted relationship between AI adoption and labour-market outcomes using a mixed-methods experimental approach that integrates quantitative econometric analysis with qualitative thematic assessment. Sectoral and regional data reveal that AI adoption is associated with substantial productivity growth and the emergence of new job categories, particularly in high-skill and technology-intensive sectors. At the same time, significant job displacement is observed in routine and low-skill occupations, confirming the presence of skill-biased technological change. The results further indicate widening wage differentials and increased income inequality in regions experiencing rapid AI diffusion. However, empirical evidence shows that reskilling initiatives and active labour-market policies play a crucial role in mitigating unemployment risks and facilitating workforce transitions. Graphical and tabular analyses consistently demonstrate that sectors investing in human capital adaptation experience more balanced employment outcomes. Overall, the findings suggest that artificial intelligence reshapes rather than eradicates employment, with its net impact contingent upon policy responses, education systems, and institutional capacity. The study contributes to ongoing discourse by providing empirical evidence that supports a nuanced understanding of AI as both a disruptive and enabling force in the modern labour market, underscoring the importance of inclusive and adaptive strategies for sustainable economic development.
Artificial intelligence, employment dynamics, job displacement, workforce reskilling, labour market transformation, automation economics
Copyright (c) 2025 Saad Abdullah, Rizwan Ullah (Author) https://creativecommons.org/licenses/by/4.0
VETERINARY EDUCATION IN THE AGE OF DIGITAL LEARNING: EXPLORING THE INTEGRATION OF VIRTUAL REALITY AND SIMULATION IN TRAINING
<span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The rapid advancement of digital technologies has reshaped health sciences education, with virtual reality (VR) and simulation emerging as powerful tools in veterinary anatomy training. This study employed an experimental mixed-methods design to evaluate the educational effectiveness of VR-enhanced anatomy instruction compared with conventional teaching approaches. Quantitative analyses revealed significant improvements in post-test knowledge scores, spatial visualization accuracy, and normalized learning gains among students exposed to immersive VR modules. Enhanced engagement levels and efficient task performance were also observed, indicating strong learner involvement and improved procedural understanding. Cognitive load measures remained within optimal ranges, suggesting that immersive learning supported comprehension without inducing excessive mental strain. Qualitative findings further corroborated these results, with students reporting increased motivation, improved conceptual clarity, and greater confidence in applying anatomical knowledge to clinical contexts. The integration of interactive three-dimensional models enabled repeated, risk-free exploration of complex anatomical structures, addressing ethical, logistical, and accessibility challenges associated with cadaver-based instruction. Overall, the results demonstrate that VR-based anatomy education offers a pedagogically robust, cost-effective, and ethically sound alternative to traditional methods. The study concludes that immersive technologies can play a central role in modernizing veterinary curricula, enhancing learning outcomes, and better preparing students for contemporary clinical practice.
Virtual reality, Veterinary anatomy education, Simulation-based learning, Extended reality, Spatial cognition, Digital pedagogy
Copyright (c) 2025 Sami Ullah, Rabia Nasir (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPING AI-POWERED DIAGNOSTIC TOOLS FOR EARLY DETECTION OF PATHOGENS IN DAIRY CATTLE TO IMPROVE HERD HEALTH MANAGEMENT
<span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The increasing adoption of digital technologies in the dairy sector has created new opportunities for precision health management through artificial intelligence–based analytics. This study evaluated an experimental, mixed-methods framework for early pathogen detection in dairy cattle using AI-driven analysis of multi-source data, including physiological measurements, behavioral indicators, production metrics, and environmental variables. Quantitative results demonstrated that AI-derived risk scores were strongly associated with early changes in body temperature, activity levels, feed intake, and milk yield, enabling the identification of disease risk prior to clinical manifestation. Visual and statistical analyses confirmed the consistency, robustness, and generalizability of predictive models across herds and conditions, while herd-level aggregation revealed clustering patterns critical for outbreak prevention. Qualitative validation by veterinary experts further supported the clinical relevance and practical utility of AI-based predictions. Overall, the findings indicate that AI-powered diagnostic systems significantly enhance early disease detection, support proactive interventions, and improve decision-making at both individual and herd levels. The integration of continuous monitoring and advanced analytics reduces diagnostic delays, lowers dependence on broad-spectrum antibiotics, and promotes sustainable dairy farming practices. This study highlights the potential of artificial intelligence to fundamentally reshape livestock health management by improving animal welfare, productivity, and economic resilience within modern dairy systems.
Artificial intelligence, precision livestock farming, dairy cattle health, early pathogen detection, machine learning diagnostics, herd health management
Copyright (c) 2025 Muhammad Ali Khan, Fatima Noor (Author) https://creativecommons.org/licenses/by/4.0
ASSESSMENT OF THE LONG-TERM IMPACT OF CLIMATE CHANGE ON LIVESTOCK PRODUCTIVITY AND BREEDING SYSTEMS IN ASIA
Climate change poses a growing threat to global livestock systems by intensifying thermal stress and disrupting physiological, productive, and reproductive functions in cattle. This study experimentally investigated the effects of temperature and humidity variability on cattle productivity and breeding performance across multiple Asian agro-climatic zones using a mixed-method research design. Quantitative field experiments assessed environmental conditions, Temperature–Humidity Index (THI), physiological responses, and productivity indicators, while qualitative analyses captured farmer and expert perspectives on adaptive capacity. The results demonstrate a strong negative relationship between increasing THI and milk yield, accompanied by significant increases in respiration rate and body temperature, confirming the severity of climate-induced thermal stress. Indigenous cattle consistently exhibited greater tolerance to heat stress compared with crossbred animals, maintaining relatively stable productivity under high THI conditions. Seasonal analyses revealed that humidity amplifies thermal stress effects, accelerating productivity losses during hotter periods. Evaluation of adaptation interventions showed that improved housing, nutritional adjustments, and climate-resilient breeding strategies significantly reduced stress impacts and improved performance. The study highlights the importance of integrating genetic resilience, management innovations, and precision monitoring to enhance livestock sustainability. These findings provide critical evidence to inform climate-smart livestock policies and breeding programs aimed at safeguarding productivity, animal welfare, and food security under future climate change scenarios.
Copyright (c) 2025 Sajjad Mehdi, Shazia Khalid (Author) https://creativecommons.org/licenses/by/4.0
PHARMACOLOGICAL CHEMISTRY OF ANTIVIRAL DRUGS: MECHANISMS OF ACTION AND CHALLENGES IN TREATING EMERGING VIRAL DISEASES
Viral diseases transgress the engraved phenomenon of modern day disease a pandemic, that calls for the advent of proper antiviral therapeutics; for a medicine or state of medicine that functions by targeting some parts or components of the virus, inhibiting its replication, and modulating immune responses of the host. They have serious drawbacks such as drug resistant ones, alongside being toxic and mutation induction by virus; all of which can challenge the use of antiviral agents in the long term. This study investigates the science related to the pharmacological chemistry of antiviral drugs, their mechanisms in action, and hurdles in treating emerging viral diseases.By utilizing in vitro and in vivo models, we will demonstrate with antiviral agents - polymerase inhibitors (remdesivir), protease inhibitors (lopinavir/ritonavir), and fusion inhibitors (enfuvirtide) - the results show that these agents can highly reduce viral replication; however, their effect is diminished due to mutations on viral proteins. Also, looking on the new approaches, CRISPR-based antivirals and RNA interference therapies, could be investigated to counteract resistance. This pens a big avenue in chemical-modification field for improvement of drug stability as well as reduced toxicity. One major outcome is increased efficacy for therapy via association of drugs by minimal resistance mechanisms. Future thrusts should be directed toward structure-based drug design and host-targeting therapies to improve effectiveness of antivirals. The study indicates how much need there is for novel antiviral drug development for emerging and re-emerging threats emanating from viral organisms.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
MECHANISTIC STUDY OF METAL ION HOMEOSTASIS AND ITS DISRUPTION IN NEURODEGENERATIVE DISEASES
Disruption in metal ion homeostasis has emerged as a critical factor in the onset and progression of neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). This study presents a comprehensive mechanistic investigation into the role of dysregulated iron (Fe), copper (Cu), zinc (Zn), and manganese (Mn) in neuronal degeneration. Using post-mortem brain tissues from affected and control individuals, we quantified region-specific metal ion concentrations and observed significant iron and manganese accumulation in the substantia nigra and hippocampus of diseased brains. Gene expression profiling revealed that DMT1 and ferroportin were significantly upregulated in AD and PD, whereas ATP7A and ATP7B were downregulated, indicating impaired copper trafficking. Oxidative stress markers—including reactive oxygen species (ROS), malondialdehyde (MDA), and 4-hydroxynonenal (4-HNE)—were markedly elevated in all disease groups, with ROS levels nearly doubling in AD compared to controls. Immunohistochemical analysis confirmed the overexpression of metal transporters in disease tissues. Furthermore, binding assays demonstrated that neurotoxic protein aggregates such as amyloid-β, tau, and α-synuclein exhibited strong affinity for Fe and Zn, suggesting a role in catalyzing oxidative damage and protein misfolding. Computational pathway modeling further highlighted the disruption of key metal-regulatory networks. Collectively, these findings establish a causal link between altered metal ion regulation, oxidative injury, and protein aggregation in neurodegeneration. The study not only elucidates the molecular underpinnings of metal-induced neuronal toxicity but also identifies potential targets for therapeutic intervention, including metal chelators and transporter regulators. These insights underscore the urgent need to develop disease-modifying strategies that restore metal ion balance in the brain.
UNDERSTANDING THE ROLE OF INDIGENOUS FALLOW MANAGEMENT SYSTEMS IN SOIL FERTILITY RESTORATION AND PEST CONTROL
This research investigates the ecological functionality and sustainability potential of indigenous fallow management systems through a combination of field experiments and participatory farmer interviews. The findings reveal that fallow practices—especially those involving legumes, shrubs, and mixed vegetation—significantly improve soil organic matter, nitrogen content, microbial biomass, and biodiversity. Table-based results highlight that shrub-integrated systems yielded the highest nutrient availability, while legume-based systems showed superior pest suppression. Figure analyses further underscore the ecological benefits of diversified fallow vegetation in supporting predator populations and stabilizing soil ecosystems. Natural pest control was shown to be highly effective through life cycle disruption and the enhancement of predator habitats, minimizing dependence on synthetic agrochemicals. The study also emphasizes the critical role of indigenous knowledge in tailoring fallow management to local environmental and cultural contexts, thus reinforcing its relevance for agroecological transitions. By demonstrating the multifunctional benefits of fallow systems—ranging from soil fertility restoration to biodiversity enhancement—this research supports the integration of indigenous practices into modern regenerative agricultural strategies. It concludes that the revival and formal inclusion of indigenous fallow systems in agricultural planning can significantly advance the goals of sustainability, resilience, and food security in ecologically challenged farming regions.
IMPACT OF AIR POLLUTION ON RESPIRATORY FUNCTION IN URBAN SCHOOL-AGED CHILDREN
Chronic exposure to urban air pollution poses a significant risk to pediatric respiratory health, yet microenvironmental exposure–response relationships remain underexplored. In this cross‐sectional study of 200 schoolchildren (mean age 9.1 ± 1.8 years; 51% male), we quantified ambient PM₂.₅, NO₂, and O₃ at five high‐traffic urban schools over four weeks and assessed lung function via spirometric FEV₁ and FVC z-scores. Mean pollutant concentrations varied markedly by site—PM₂.₅ ranged from 28 to 50 µg/m³, NO₂ from 35 to 55 ppb, and O₃ from 25 to 32 ppb. Multivariable linear regression, adjusted for age, sex, BMI, and passive smoking, revealed that each 1 µg/m³ increase in PM₂.₅ was associated with a 0.012 decrease in FEV₁ z-score (95% CI: –0.015 to –0.009; p < 0.001), while NO₂ and O₃ increases of 1 ppb corresponded to FEV₁ z-score reductions of 0.010 (95% CI: –0.013 to –0.007; p < 0.001) and 0.008 (95% CI: –0.011 to –0.005; p = 0.002), respectively. Similar inverse associations were observed for FVC. Sensitivity analyses across alternative exposure windows and model specifications confirmed the robustness of these findings. These results underscore the vulnerability of developing lungs to pollutant exposures in school environments and highlight the need for targeted mitigation—such as traffic management, green buffers, and stricter emission controls—around schools. Routine lung‐function screening and educational interventions may further protect at-risk children. Future longitudinal studies should evaluate long-term respiratory trajectories and intervention efficacy to inform policy and safeguard urban pediatric populations.
CLINICAL AND GENETIC PREDICTORS OF STROKE RECURRENCE AFTER ISCHEMIC EVENTS
Stroke recurrence remains a leading cause of disability and death among ischemic stroke survivors. In a prospective cohort of 500 adults (aged 45–85 years) with first-ever ischemic stroke, we evaluated the combined utility of clinical risk factors and a genome-wide polygenic risk score (PRS) for predicting recurrent events over 12 months. Baseline prevalence of hypertension (68%), diabetes (24%), dyslipidemia (57%), and atrial fibrillation (18%) was consistent with high-risk profiles. In univariable Cox analyses, hypertension (HR 1.80, 95% CI 1.50–2.15), atrial fibrillation (HR 2.10, 95% CI 1.65–2.65), diabetes (HR 1.45, 95% CI 1.10–1.90), and each SD increase in PRS (HR 1.25, 95% CI 1.10–1.42) were all significant predictors (p < 0.01). Patients in the highest PRS tertile experienced a 25.1% one-year recurrence rate versus 9.0% in the lowest tertile (HR 2.90, 95% CI 1.70–4.95; p < 0.001). A combined clinical-genetic Cox model achieved a C-index of 0.81 and demonstrated superior calibration compared with clinical-only (C-index 0.72) or genetic-only (C-index 0.68) models. Random forest analyses corroborated the primacy of atrial fibrillation and hypertension but also ranked PRS, age, and diabetes among the top five importance metrics. Semi-structured interviews (n = 40) identified medication adherence barriers and lifestyle modification challenges as prevalent patient-reported obstacles to effective secondary prevention. Our findings support an integrative risk-stratification paradigm that merges genomic profiling with traditional clinical assessment to identify high-risk survivors for targeted interventions—such as intensified blood pressure control, novel antithrombotic therapy, and adherence support—to reduce recurrent stroke burden.
ADVANCEMENTS IN PERSONALIZED MEDICINE: TAILORING TREATMENT FOR GENETIC DIVERSITY
Individualized medicine has become an innovative trend in the health sector, and it provides custom treatment plans depending on individuals genetic, environmental, and lifestyle variations. With the developing pace of improvements in the field of genomics and molecular diagnostics, molecular data regarding individual patients are changing the process of clinical decision-making and approaches to achieving optimal therapeutic effects. In this study, the use of genomic technologies, including next-generation sequencing (NGS), CRISPR-Cas9 gene therapy, and high-throughput genomic screening in the process of developing precision treatment approaches to complex diseases is discussed.Evidence based on genetic variants, pharmacogenomic information and actual clinical application were reviewed systematically to assess the effect of genetic-diversity on susceptibility to disease and reactions to various drugs. The research synthesizes the evidence in precision oncology, cardiovascular pharmacogenomics, management of metabolic disorders, which allows defining actionable genetic markers and demonstrating their practicality in therapy stratification.Based on such mutations as EGFR, BRCA1, and CYP2C19, the outcomes show a high rate of variability in treatment response. Interventions matched to genotype were more effective and associated with fewer adverse drug reactions generally, and when both clinical decisions and pharmacogenomic profiles were used in decision. The adoption of individualized genomic technologies has grown immensely during the last 10 years, which affirms their clinical importance. Moreover, predictive analytics based on AI showed great promise in increasing the accuracy of diagnosis and designing optimal treatment plans.To sum up, personalized medicine is the shift in a paradigm toward more preventive, predictive, and participatory medicine. Although its application is hampered by its ethicalities, integrity of data, and healthcare inequity problems, the advantage it has on patient outcomes and treatment effectiveness is significant. In further light of the role of genomic science, artificial intelligence, and regulatory conditions, the future of regional and global health systems is on the verge of being reshaped by personalized medicine.
EXPLORING MICROBIAL GENOMICS AND METABOLIC PATHWAYS FOR SUSTAINABLE BIOTECHNOLOGICAL INNOVATIONS
The rapid expansion of microbial biotechnology has transformed approaches to environmental sustainability, industrial processing, and bioremediation. Central to this transformation is microbial genomics, which enables the comprehensive analysis of microbial DNA to uncover functional traits, metabolic pathways, and pollutant-degrading capabilities. In this study, an integrated framework leveraging genomic sequencing, metabolic pathway engineering, and multi-omics analysis was implemented to explore the biotechnological applications of microbial systems.Our methodology involved sequencing microbial genomes, annotating functional genes, and applying CRISPR and synthetic biology techniques to engineer improved strains. We also utilized transcriptomic, proteomic, and metabolomic datasets to assess metabolic activity and optimize processes such as biofuel production, pollutant degradation, and microbial fuel cell efficiency. A series of statistical evaluations and visualizations were conducted to benchmark the engineered strains against conventional systems in terms of metabolic yield, stress response, and pollutant removal efficacy.The results demonstrated a significant increase in biofuel yield (up to 42%) in modified strains and enhanced pollutant degradation efficiency by over 60% in controlled environments. Omics-driven optimization showed consistent metabolic stability across diverse conditions, with protein expression profiles supporting enhanced enzyme activity and metabolite turnover. These findings affirm the value of genome-informed microbial design in advancing sustainable technologies.In conclusion, this study highlights the transformative potential of microbial genomics and multi-omics integration in reshaping industrial and environmental biotechnology. The engineered microbial systems not only exhibit high efficiency and adaptability but also offer scalable and eco-friendly alternatives for energy production, waste treatment, and ecological restoration. As regulatory frameworks and ethical considerations evolve, the insights from this research can guide safe, impactful deployment of microbial innovations across global sectors.
COGNITIVE BEHAVIORAL THERAPY: TRANSFORMING NEGATIVE THINKING AND PROMOTING MENTAL HEALTH
One of the most common psychotherapeutic approaches is the Cognitive Behavioral Therapy (CBT), which has been proven to treat different psychological disorders due to its focus on the relationship that exists between mind, feelings, and actions. Based on the cognitive models constructed by Beck and Ellis, the idea behind CBT is to help patients take control of their unconstructive thoughts and substitute them with balanced, objective ideas.This paper examines the underlying theoretical concepts, underpinning principles of CBT and the emerging practices such as cognitive restructuring, behavioural activation, exposure treatment and mindfulness-based cognitive treatment (MBCT). In the methodology, it will be used both traditional and digital methods of conducting the CBT and more emphasis will be placed on following the structural methods of interventions, goal-setting strategies and techniques of building skill to tackle anxiety, depression, phobias, and any other kind of disorder at different age levels.The results with CBT show a high decrease in anxiety and depression measures and increase in engagement and adherence via digital systems as well as positive functional outcomes were observed in clinical populations. It is of note that the involvement of patients in the CBT was linked to elevated resiliency, enhanced emotional control, and reductions in recurrent depressive setting relapse. Moreover, the flexibility of CBT in children, adolescents, and older adults was also exhibited, and, in each of the groupings, progress in cognitive restructuring and behavioral results was measurable.To summarize, CBT is one of the pillars of evidence-based psychological therapy as it can be considered profound and broadly applicable to the treatment of emotional disorders. In spite of the problems of lack of efficacy in the treatment of complex or chronic psychopathologies, persistent innovations, especially through digital CBT and the inclusion of mindfulness, have extended its accessibility and effectiveness in the long-term. This study confirms the more-than-timely applicability of CBT to the contemporary mental health care provision and the necessity of further development with the use of technological features and flexible treatment methods.
THE ROLE OF PHARMACOVIGILANCE IN ENSURING DRUG SAFETY
Pharmacovigilance has become an essential component of the policy on the state of the population since the increasing pharmaceutical complexity and the development of extensive pharmaceutical use by people. Because adverse drug reactions (ADRs) remain one of the main causes of morbidity and a healthcare burden in most countries, effective post-marketing surveillance provisions are necessary to identify the occurrence of a variety of risks that may accompany the use of medicinal products.The study has a multi-pronged methodology through the spontaneous reporting part, cohort follow-up and case control assessments, which were complemented with the help of advanced statistics and artificial intelligence-backed algorithms. An organized framework to recognize the trends, demographic correlations and measure ADR severity both using traditional approaches and real-world data mix was created.Findings demonstrated that there was a significant growth in the number of ADR reports with time and that the rates of incidences varied between different classes of drugs like antibiotics and antidepressants. The disparity occurred in terms of age and gender and the results varied between mild and severe and a disproportionate risk of certain populations. The AI-assisted systems proved to be more effective than traditional ones in the process of creating a signal or a predictive model in the early discovery of the potential safety issues and allowed finding them quicker. The change of reporting behaviors also differed considerably within the healthcare professionals and patients which denotes using more diverse and accessible pharmacovigilance platforms.To summarize, the research emphasizes the current change of pharmacovigilance role in the contemporary healthcare environment and the necessity to provide international unification of safety requirements. The advancements in technologies provide the unprecedented opportunities to improve ADR monitoring, and the systemic issues like underreporting or inequality of infrastructure should be overcome. To create effective patient protection policies globally, regulatory frameworks have to be toughened, stakeholder agencies have to be encouraged, and intelligent surveillance tools need to be onboarded to help build a responsive, robust pharmacovigilance environment.
THE ROLE OF PERSONALIZED MEDICINE IN CARDIOVASCULAR DISEASE MANAGEMENT
Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, imposing substantial health and economic burdens. Traditional treatment approaches often fail to account for individual variability in genetics, environment, and lifestyle, leading to suboptimal outcomes and adverse drug reactions. Personalized medicine, also referred to as precision medicine, offers a transformative alternative by tailoring treatment strategies to the unique biological profile of each patient.This study employs a multidisciplinary methodology integrating genetic testing, pharmacogenomics, and biomarker profiling to assess the impact of personalized interventions in cardiovascular care. Data were synthesized from recent clinical trials, real-world case studies, and computational modeling to evaluate therapeutic efficacy, adverse event reduction, and cost-effectiveness in genetically stratified patient populations.The results demonstrate that personalized medicine significantly improves clinical outcomes in CVD management. Genotype-guided drug prescriptions enhanced treatment responses and reduced incidences of drug-related side effects, particularly in therapies involving statins and warfarin. Biomarker-guided interventions further optimized heart failure treatment by enabling early detection and targeted therapy adjustments. Additionally, the integration of machine learning models effectively stratified patients by predicted drug response, supporting data-driven decision-making in clinical practice.In conclusion, personalized medicine enhances the precision, safety, and efficiency of cardiovascular disease management. By leveraging advancements in genomics, molecular diagnostics, and computational analytics, this approach enables clinicians to deliver tailored treatments that improve patient outcomes while minimizing risks. Despite existing challenges related to cost, regulatory frameworks, and clinical adoption, the continued integration of personalized strategies holds substantial promise for reshaping the future of cardiovascular healthcare.
ANALYZING THE GUT-BRAIN AXIS IN ALZHEIMER'S DISEASE: A MICROBIOME-CENTRIC APPROACH
The gut-brain axis has emerged as a crucial biological network influencing the onset and progression of Alzheimer’s disease (AD). This study explored the relationship between gut microbiota dysbiosis and AD-related neurodegeneration by synthesizing evidence from recent literature. The findings revealed that individuals with AD demonstrated significantly lower microbial diversity, higher systemic inflammation (IL-6, TNF-α, CRP), and disrupted metabolite levels (e.g., SCFAs, tryptophan) compared to healthy controls. Additionally, neurotransmitters critical for mood and cognition, including serotonin, dopamine, and GABA, were markedly diminished in the AD group. These alterations correlated strongly with cognitive deficits, as measured by standardized tests such as MMSE, MoCA, and ADAS-Cog. Interventional strategies targeting the microbiome—such as probiotics, prebiotics, and dietary modifications (notably the Mediterranean diet)—were associated with improved gut composition and better cognitive performance. Probiotic treatments like Lactobacillus and Bifidobacterium improved both gut diversity and MMSE scores. Dietary interventions reduced CRP levels and enhanced cognitive outcomes. Figures and tables presented in the study support the hypothesis that gut-brain axis modulation may serve as a viable adjunctive or preventive strategy in AD treatment. This research highlights the need for a paradigm shift from a purely neurocentric model to one that integrates microbial ecosystems as key regulators of brain health.
Copyright (c) 2025 Hassan Yar Mahsood , Abdul Ghaffar, Roohan Ahmad, Muhammad Rehan, Muhammad Inam Farooq (Author) https://creativecommons.org/licenses/by/4.0
QUANTIFYING THE CONTRIBUTION OF ORGANIC AMENDMENTS TO LONG-TERM SOIL CARBON SEQUESTRATION AND FERTILITY
This study evaluated the effects of various organic amendments—including compost, manure, biochar, and chitin—on soil health, microbial activity, and crop performance under field conditions. A mixed-methods approach combining quantitative soil analysis and plant growth measurements with qualitative stakeholder input was employed. Results showed that organic amendments significantly improved soil pH, microbial biomass carbon, nitrogen mineralization, and enzymatic activities. Among all treatments, chitin-based amendments consistently outperformed others, resulting in the highest increases in microbial biomass (300 mg/kg), nitrogen mineralization (5.2 mg/kg/day), and plant biomass yield. Enhanced nutrient uptake and enzymatic activity were also observed, particularly in urease and dehydrogenase assays. The application of microbial inoculants alongside organic amendments promoted further improvements in nutrient absorption, root development, and disease resistance. Notably, disease incidence was reduced by up to 45% under chitin treatment, indicating strong biocontrol potential. Visual analyses reinforced these findings, with significant differences in soil and plant parameters across treatments. The results support the integration of organic amendments as a sustainable solution for enhancing soil fertility and crop yield while minimizing dependence on synthetic inputs. This study highlights the ecological and agronomic benefits of organic matter incorporation and advocates for biologically based soil management strategies in sustainable agriculture.
Copyright (c) 2025 Irfan Ahmad , Muneeba, Muhammad Suleman Aziz (Author) https://creativecommons.org/licenses/by/4.0
COLOSTRUM MANAGEMENT EFFECTS ON PASSIVE IMMUNITY TRANSFER IN CALVES
This paper discusses how methods of colostrum management might influence the passive immunity transfer in neonatal calves with particular focus on the variables; colostrum quality, time of feeding, hygiene and subsequent health outcomes in the calves. Determining the IgG levels in 120 colostrum samples and the corresponding calf serum samples was done by conventional ELISA methods. The results revealed a significant difference in the quality of colostrum between breeds and parities with multiparous Holstein cows having the highest mean concentrations of IgG. The timeliness of colostrum provision was quite crucial. Passive immunity transmission (serum IgG >10 g/L) success rate was 92 percent in calves fed within two hours of birth and only 48 percent in calves fed after six hours. Also, high bacterial levels in colostrum (>100,000 CFU/mL) were associated with a significantly low absorption efficiency of IgG, which explains the importance of hygienic treatment. Calves that acquired adequate passive immunity well had significantly lower morbidity rates, approximately 14 percent experienced diarrhea and 9 percent respiratory infection as compared to 33 percent and 28 percent in calves that had failed transfer. What the results of weight gain during the initial 30 days showed, was that even more, the growth is associated with elevated blood IgG levels. These findings demonstrate the significance of managing the colostrum properly that involves feeding the calves at the correct age, ensuring that the colostrum quality is high, and maintaining cleanliness. This matters in the wellbeing of the calves and farm production. The research gives credence to the use of practical, science-based colostrum policies as a cornerstone of herd health programs
Copyright (c) 2025 Fawad Ahmad, Muhammad Asif (Author) https://creativecommons.org/licenses/by/4.0
Chemical Modulation of Host-Pathogen Interactions: Investigating the Role of Secondary Metabolites in Bacterial Virulence and Host Immunity
The intricate chemical communication between bacterial pathogens and host immune systems plays a pivotal role in determining infection outcomes. This study investigates the immunomodulatory functions of bacterial secondary metabolites—specifically pyocyanin, staphyloxanthin, and enterobactin—by integrating metabolite profiling, host immune response assays, and in vivo infection models. Metabolites were isolated and characterized using LC-MS and NMR, followed by functional assays on THP-1 macrophages and murine splenocytes. Cytokine quantification revealed significantly elevated IL-6 and TNF-α levels in pyocyanin-treated cells, with IL-6 reaching 320 pg/mL and TNF-α 420 pg/mL, compared to 45 pg/mL and 60 pg/mL in controls, respectively. Gene expression analyses indicated substantial fold changes for immune markers IL6 (5.4×), TNFA (6.1×), and CD80/CD86 in response to pyocyanin, suggesting strong immune activation. In vivo survival assays demonstrated that Galleria mellonella and BALB/c mice had the lowest survival rates post-exposure to pyocyanin (35% and 40%, respectively), highlighting its virulence-enhancing potential. Additionally, ROS generation and phagocytosis assays revealed a sharp rise in oxidative stress (820 MFI) alongside decreased phagocytic efficiency (28%) in pyocyanin-treated groups. Enterobactin and staphyloxanthin elicited moderate effects, underlining metabolite-specific immune modulation. These findings confirm that secondary metabolites actively modulate host immunity, not merely as virulence enhancers but as key biochemical modulators in infection dynamics. This research provides novel insights into how targeting these chemical agents may serve as an effective anti-virulence strategy in the fight against antibiotic-resistant infections.
Copyright (c) 2025 Mashal Shahzadi , Rooh Ullah (Author) https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING MODELS FOR PREDICTING SEPSIS ONSET IN ICU PATIENTS BASED ON EHR DATA
Early detection of sepsis remains a clinical priority in intensive care units due to its association with high morbidity and mortality rates. This study presents a machine learning-based framework for predicting sepsis onset using electronic health record data, with the goal of enabling timely clinical intervention. Multiple algorithms—including ensemble models, support vector machines, and linear classifiers—were trained on a dataset comprising vital signs, lab values, medications, and demographics. Results demonstrated that ensemble models achieved superior predictive performance, with significant gains in accuracy, precision, recall, and F1-score after hyperparameter tuning. Key predictive features identified included lactate concentration, respiratory rate, and white blood cell count. Visualization tools such as SHAP values, temporal risk plots, and feature importance charts enhanced the interpretability of the models. Comparative analysis with conventional clinical scoring systems revealed that the machine learning models offered improved sensitivity and earlier detection. The study also highlights critical challenges such as data heterogeneity, algorithmic transparency, and clinician adoption barriers. Despite these challenges, the findings underscore the transformative potential of machine learning in critical care, particularly in establishing real-time early warning systems for sepsis. The integration of interpretable and accurate models into clinical workflows could significantly reduce diagnostic delays and improve patient outcomes. This research contributes to the advancement of precision medicine by leveraging artificial intelligence for real-world clinical decision support.
Copyright (c) 2025 Muhammad Rehan , Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
VETERINARY BIOMECHANICS AND INJURY PREVENTION IN RACEHORSES
This paper examines biomechanical factors that expose racehorses to the risk of being injured and etal analyses of how to ensure that they are not injured. We investigated biomechanics of locomotion locomotor mechanics and ground reaction force (GRF) at high speed among Thoroughbreds in training in various environments with the use of high-speed kinematic analysis and GRF platforms. The high vGRF and irregular limbs greatly increased the probability of horses in having joint and tendon issues. Indeed, wearable sensors allowed constantly monitoring gait and detecting biomechanical alterations that occur when one is fatigued at their earliest stages. It was found by statistical study, that measures such as surface modulation and physiotherapy, which are specifically tailored to the individual, reduced injury levels by 42%. A model based on limb loads, stride variability and angular joint kinematics in predicting injury risk was 85 percent accurate. Such findings indicate the value of real-time biomechanical profiling as a way of enhancing performance of racing horses and preventing major injuries.
Copyright (c) 2023 Abdul Wadood Jan, Shafiq Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
MOLECULAR PATHOGENESIS OF ZOONOSES IN URBAN WILDLIFE
Due to growing human-wildlife contacts and disturbance in the environment, wildlife-borne zoonotic diseases originated in urban areas are proving to be of a more concern to the population health. They used 300 specimens of rodents, birds, and mesocarnivores in the urban setting to investigate the molecular pathogenesis of major zoonotic agents such as Leptospira spp., Salmonella enterica, Bartonella henselae, and urban-adapted coronaviruses. We have found the encoders of genes [lipL32, invA, S1 spike protein], which play a role in virulence, to be abundant using the methods of PCR, ELISA, and metagenomic sequencing. According to the comparison between the urban isolates and their rural counterparts, the former had greater host adaptation and greater gene expression, implying that the evolution of the pathogen might proceed even faster under urban stressors. Host immune profile identified asymptomatic carriage in wildlife, which also means the competence of a reservoir lacking overt disease. Spatial mapping featured a likelihood of spill over, leading to the association of densely populated regions where the pathogen was highly loaded. Due to these results, a One Health molecular surveillance paradigm where wildlife disease surveillance, urban ecology and human public health preparedness are integrated is necessary.
Copyright (c) 2023 Aftab Ahmed, Mukhtar Ahmad, Shahid Iqbal, Muhammad Usman (Author) https://creativecommons.org/licenses/by/4.0
HYBRID AQUATIC ANIMAL DISEASE MANAGEMENT STRATEGIES
This paper potential researches into the management of diseases in aquatic animals through mixed methods. It mixes experimental therapies with both quantitative and subjective evaluation to look at the most suitable methods of facilitating health and maintaining operations. There were three treatments among fish and crustacean in a controlled aquaculture conditions: Treatment A, Treatment B, and Treatment C. The baseline measurements included quality of the water, the level of pathogens, the immunological markers and growth rates. The Treatment A showed the highest survival rates, the fastest recovery and had the most significant growth performance within the 30-day follow up period. Treatment B, however, performed best in enhancing the immune response and maintaining the level of water quality steady. Feeding conversion efficiency of C treatment was improved, and this is advantageous to large organizations since money is saved. By performing ANOVA and regression analysis of the data a clear difference between the treatments was indicated which was substantial. Important correlations between the environmental variables and health indicators were also found in graphical visualisations. The professionals working in aquaculture wrote qualitative feedback that assisted us to comprehend the effectiveness of each type of therapy. The integration of such data favours a hybrid management approach which involves the utilisation of the most effective elements of the three therapies to facilitate survival, resilience, and efficiency in the operative process. These findings demonstrate that many aspects should be implemented in aquaculture to control diseases. The tactic is also useful in enhancing productivity, profits, and environment compatibility in the long run.
Copyright (c) 2023 Muhammad Waqar Ali (Author) https://creativecommons.org/licenses/by/4.0
HYBRID MODELING OF ECOSYSTEM-LEVEL METABOLISM
Metabolic prediction and management has significant funding to manage change at the ecosystem level because it involves management of the environment. The proposed research will present a hybrid modeling design where field flux measurements can be coupled with remote sensing indices and machine learning to characterize and support predictions of biogeochemical processes in diverse ecosystems. We received information on forest, wetland, estuarine and grassland biomes including GPP, NEP, ER and nutrient fluxes. Non-linear relationships between environmental variables were determined using Random Forest and XGBoost algorithms and mechanistic carbon modeling. The model outperformed R 2 = 0.85 at every location and the SHAP analysis revealed that the most significant predictors were NDVI, temperature, and soil moisture. These findings are supported by 9 big tables and 12 high quality images that demonstrate the way by which carbon, and nutrients flow space- and time-wise. Experts, interviewed, confirmed that the outcomes of the models can be applied in real life practices in the monitoring of the environment and policy formulation. Combining mechanistic and data-driven approaches with respect to obtaining a complete picture of the metabolism of ecosystems: this work demonstrates just how useful such an integrated method can be. This may be of use when it comes to future climate-resilient management plans.
Copyright (c) 2023 Zia Ur Rehman, Mashal Shahzadi (Author) https://creativecommons.org/licenses/by/4.0
STEM CELL BIOLOGY AND REGENERATIVE TRANSCRIPTOMICS
<span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; color: black; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;"> <span style="font-size: 9.0pt; mso-bidi-font-size: 10.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">There is increasingly the reliance on a fusion of stem cell biology and transcriptomics to find out how the cells and tissues heal themselves, and how they can be induced to be reprogrammed using regenerative medicine. This experiment adopts a mixed design, whereby a thorough high-throughput transcriptome profiling, practical functional regenerative assays, and single-cell resolution will be used to explore how stem-cell derived lineages may be regenerated. Mesenchymal, neuronal, and epithelial has been generated when scientists converted pluripotent stem cells to other cells. Then they placed the cells in circumstances that appeared to be harmed, such as an experience of oxidative pressure and the activation of inflammation. The results of bulk RNA-Seq and single-cell RNA-Seq experiments demonstrated that key regenerative markers such as FGF2, IGF1, VEGF-A, and BMP7 were also being activated in all cases. This was highly associated with increased rates of wound healing and migration. Tables revealed expression stability, differentiation scores and regeneration index between various lineages and stress circumstances. Tables 6 and 9 revealed genes that had high expression levels and which were linked with superior regenerative phenotypes. The figures provided the evidence of how the co-expression varies with time, how the changes in transcriptome are specific to individual lineages, and how regeneration capabilities are dispersed, using line, scatter, hybrid, and pie charts. In case, hybrid plots (Figures 4 and 12) presented an excellent job in demonstrating the relationship between gene expression, differentiation and regeneration efficacy in more than one manner. The statistical approach of developing PLS regression to generate statistical models demonstrated that transcriptome factors could efficiently foretell functional regeneration outcomes. As regulated, the outcomes demonstrate that regenerative transcriptome statuses are characterized by lineage-sensitive gene sets. One can use these signatures both as a predictor and a medicine. This finding provides us with a paradigm scaleable and repeatable that links molecular discoveries to their phenotypical consequences. That will enable us to develop more specific regenerative technology through stem cell technology.
Copyright (c) 2023 Muhammad Asadullah Usman, Irum Habib (Author) https://creativecommons.org/licenses/by/4.0
MULTIDISCIPLINARY PERSPECTIVES ON CLIMATE CHANGE: SOLUTIONS FOR A SUSTAINABLE FUTURE
<span style="font-size: 9.0pt; mso-bidi-font-size: 10.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">Climate change presents a complex and pressing global challenge, with profound implications for ecosystems, economies, and public health. Addressing this crisis requires an integrated, multidisciplinary approach that bridges environmental science, economics, sociology, and technological innovation. This study synthesizes empirical findings, policy analyses, and real-world case studies to examine how diverse disciplines contribute to effective climate change mitigation and adaptation strategies. Methodologically, the research employs a combination of literature review, cost-benefit modeling, technology adoption analysis, and community-level adaptation evaluations to construct a comprehensive understanding of cross-sectoral responses.Results demonstrate that renewable energy investments, particularly in solar and wind technologies, yield significant long-term economic and ecological benefits. Community-based initiatives, such as participatory forest management and agroecological practices, outperform top-down approaches by fostering local engagement and improving biodiversity outcomes. Public perception data indicate increasing global awareness of climate risks, though disparities persist based on socioeconomic and geographic factors. Additionally, technological innovations in carbon capture and digital adaptation platforms are emerging as pivotal tools for building resilience.The findings underscore that effective climate action is not solely rooted in policy or innovation, but in the synergy of knowledge systems and inclusive governance. A key conclusion of this research is that adaptive, locally contextualized strategies—supported by scientific evidence and economic rationale—are essential for fostering sustainable, long-term responses to climate change. Collaborative frameworks that integrate environmental restoration, economic planning, social mobilization, and technological advancement offer the most promise in confronting the multifaceted nature of the climate crisis. This research advocates for continued interdisciplinary collaboration and policy alignment to ensure global climate resilience and equitable development.
Copyright (c) 2023 Aftab Ahmed, muk (Author) https://creativecommons.org/licenses/by/4.0
REMOTE SENSING AND PHYSIOLOGICAL BIOMARKERS FOR DROUGHT STRESS IN FOREST TREES AND CROPS
An increased risk to the productivity of forests and farms throughout the world, drought stress is proving to be an even greater risk hence necessitating more methods of detecting and gauging them as rapidly and precisely as possible. As evidence by this study, a combination of remote sensing and physiological biomarkers can be used to locate and quantify drought stress in trees and crops growing in forests. The model identified early indications of drought in multiple spatial scales and made use of hyperspectral image, thermal remote sensing and satellite based vegetation indices (NDVI and NDWI). Meanwhile, the remotely observed drought indicators were confirmed with the help of ground-truthing the physiological data of plants, such as chlorophyll fluorescence, leaf water content, and stomatal conductance. To guess how severe a drought will be we grouped the spectral information through highly developed machine-learning approaches--particularly convolutional neural networks. It had very accurate results (R 2 = 0.87) with low values of RMSE. The scheme of the technique workflow is depicted in Figure 1 and consists of the pieces of data fusion, preprocessing, spectrum analysis, and validation. The findings indicated that strong relationships existed between remotely sensed indices and physiological responses in case of drought. The model was also able to identify the drought-tolerating and drought-intolerating plant species correctly which is valuable data under adaptive water management and precision agriculture. The given study advances the science of drought detection by offering a scaleable, non-invasive and data-driven technique in integration of remote sensing technologies with physiological science. The findings have far reaching consequences on climate change resilience of ecosystems, the enhancement of irrigation practices, and climate change resilience of ecosystems.
Copyright (c) 2023 Muhammad Umair, Muhammad Asad (Author) https://creativecommons.org/licenses/by/4.0
EVALUATING NATURE-BASED SOLUTIONS FOR FLOOD MITIGATION IN COASTAL CITIES
The rising sea levels and heavy rain have forced coastal communities to seek alternatives to eliminate flood risks. Some of these alternatives have become more popular in the recent past as they are eco-friendly solutions referred to as Nature-Based Solutions (NBS). This paper examines the effectiveness of various NBS interventions such as mangrove buffers, urban wetlands, green roofs, and bioswales. It does so by using the combination of spatial modeling, infield evaluations, and hydrology simulations. The findings indicate that NBS measures reduced the mean flood water by as much as 45 percent, maximum runoff by 38 percent and increased storm water soakage in numerous cities. The simulation-based 9 tables and 12 various visualizations revealed that the mangroves and the bioswales are effective in high-risk zones where there occurs water up to hydraulic performance as well as with regard to the cost benefit ratio. In addition, scenario modeling demonstrated that approaches that combine NBS with the existing gray infrastructure and smart technologies are more resistant. Through the study as well, it is revealed that NBS has both social and economic advantages where some of them include making the cities livable, raising biodiversity and reducing expenditures in public health costs. According to the paper, NBS ought to be a priority in resilience plans and urban policy frameworks, despite the issues regarding the implementation thereof, such as a shortage of space within urban world as well as the risk of eco-gentrification. On the whole, this work validates NBS as a useful, versatile, and multi-focal city growth strategy that may address climate change.
Copyright (c) 2023 Abdul Waheed Shah, Hammad Ur Rehman, Muhammad Waqar Ali (Author) https://creativecommons.org/licenses/by/4.0
GEOENGINEERING THE CLIMATE: ETHICAL AND ENVIRONMENTAL RISK ASSESSMENT OF SOLAR RADIATION MANAGEMENT
This paper examines aseismic Solar Radiation Management (SRM) as a comprehensive dimension of its ethical and environmental risks with emphasis on using stratospheric aerosol injection to alter the climate. By method, we took a mixed-methods approach to examine the environmental impact and ethical concerns associated with SRM. This was achieved through a mixture of quantitative modeling of the earth systems and qualitative engagement of the stakeholders. The simulation of climate models at two different SRM scenarios (5 and 10 Tg SO 2 injections) indicated that world temperatures may fall by up to 1.3 o C. Nevertheless, the variation between territories was rather large, in particular with regard to quantity of precipitation and concentration of ozone. It has been discovered that marine biodiversity and UV exposure were put in danger as marine ecosystems and ozone levels in high latitudes were affected. An Equity Deviation Index was used to gauge the impact of the various regions and it established our concerns of an environmental justice. Meanwhile, Delphi analysis and systematic interviewing in various disciplines with stakeholders revealed such ethical priorities as global justice, transparency, sovereignty, and a fair intergenerational game. The Delphi rounds revealed that an increasing number of the interviewees were convinced that we require robust precautionary principles and systems of global governance. Cross-impact matrices indicated that areas of large climatic risk and areas with greatest moral objections were in tune giving indications of governance hotspots. A governance readiness index also revealed that the capacity of institutions in the Global South to perform their jobs is also big. On the whole, these findings demonstrate that SRM can contribute to the climate in a certain respect, but it has gigantic moral and environmental expenses. According to the report, SRM can be only applied in a strictly supervised global context, in addition to, rather than as a replacement of the intensive action to reduce emissions. The proposed framework provides a model that can be employed in assessing geoengineering concepts in their entirety.
CLIMATE-INDUCED SHIFTS IN SPECIES RANGES: PREDICTIVE MODELING AND CONSERVATION IMPLICATIONS
The research involved a blended design that coordinated quantitative species distribution modelling (SDM) with MaxEnt data with the qualitative expert elicitation data conduction via Delphi analysis to predict the impacts that climate change is likely to have on the range of 50 land or marine species across the various biological zones. Temporal estimates by RCP 4.5 and RCP 8.5 mean that over 68 per cent of species are likely to shift their geography centroid by at least 200km by 2080, with majority moving in cool latitude or altitude. A reduction in the habitat suitability was found to be precipitous in those species which are acclimated to the cold climates and have low ranges particularly those exposed to RCP 8.5. Its two most significant climate predictors included temperature seasonality and variability of precipitation. Examining the percentage of models with a high degree of work (accuracy) of more than 0.85, we found that a mere figure of 88 percent is in the AUC category which makes them very correct models in predicting. Spatial overlay study revealed that gaps in conservation are large and less than 40% of the estimated species ranges fall within the existing protected areas. Professionals emphasized the significance of such adaptive measures as dynamic corridor planning and targeted assisted migration. Visual analysis such as centroid shift scatter plot, hybrid risk models and policy scenario radar charts provided us with effective information about the locations of biodiversity hotspots and priority areas of conservation in the future. According to the report, the static conservation frameworks lack effectiveness when the climatical conditions vary. Rather it proposes that climate-smart integrative solutions are needed to employ predictive modelling and real time adaptive management in combination. These findings are extremely critical in terms of planning ahead to safeguard global biodiversity since temperature changes are picking up pace.