Browsing by Author "Makieu, Peter"

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Artificial Intelligence-guided Engineered Nano-biochar for Carbon Management, Climate-smart Agriculture and Emerging Contaminant Remediation: A Critical Narrative Review
(Sciencedomain International, 2026-09-25) Makieu, Peter
Biochar sits at the intersection of biomass valorisation, carbon management, soil restoration and contaminant control, while nano-engineering and artificial intelligence (AI) are increasingly proposed as means of tailoring its properties and accelerating design. Yet the phrase “AI-driven engineered nano-biochar” risks implying a level of technological integration and field validation that the evidence has not yet achieved. This critical narrative review evaluates that intersection by separating three evidence layers: established knowledge on bulk biochar, rapidly expanding evidence on engineered and nanoscale biochar, and emerging applications of machine learning to biochar production and performance prediction. Literature published from 1 January 2000 to 19 July 2026 was appraised for methodological quality, scale, mechanistic relevance, environmental realism and claim-to-evidence alignment. The evidence is strongest for the persistence of a fraction of pyrolytic carbon, context-dependent improvements in soil properties and crop performance, and enhanced adsorption after selected physical or chemical modifications. Machine-learning studies can predict biochar yield, composition and adsorption outcomes with high within-dataset accuracy, but their external validity is constrained by heterogeneous literature-derived datasets, sparse reporting of negative results, inconsistent material characterisation and limited prospective validation. Nanostructuring can increase accessible surface area, reactive sites and dispersibility, but the same attributes raise unresolved questions about particle transport, ecotoxicity, recovery, ageing and carbon permanence. Field evidence for nano-biochar remains comparatively scarce, although recent rice and salinity studies demonstrate agricultural promise under defined conditions. For emerging contaminants, most evidence remains batch-scale and equilibrium-centred, with limited treatment of realistic mixtures, dissolved organic matter, continuous flow, regeneration and spent-sorbent management. Carbon neutrality therefore cannot be inferred from adsorption capacity, crop response or carbon content alone; it requires life-cycle accounting, durable carbon measurement, energy and reagent inventories, counterfactual biomass fate, and monitoring of downstream risks. The most defensible near-term pathway is not autonomous AI discovery, but uncertainty-aware, multi-objective decision support coupled to standardised experiments, field trials and life-cycle assessment.
Cascading Climate Tipping Points and Compound Extremes: AI-Driven Earth System Digital Twins for Anticipatory Governance
(Sciencedomain International, 2026-05-12) Makieu, Peter
Cascading climate tipping points and compound extreme events pose urgent and under-characterized threats to Earth system stability. Multiple hazards occurring simultaneously or in rapid succession can reduce effective tipping thresholds by 15–30% compared to single-stressor exposures, narrowing safety margins implied by international temperature targets. Traditional Earth System Models (ESMs) are limited in capturing nonlinear feedbacks and threshold-crossing dynamics. AI-enabled Earth System Digital Twins (ESDTs), data-assimilative, high-resolution, and continuously updated, offer transformative potential to anticipate these risks in near real time. This systematic review, conducted following PRISMA 2020 guidelines and registered in PROSPERO, synthesizes 287 peer-reviewed studies (2015–2024) across four thematic pillars: (i) cascading climate tipping points, (ii) compound extremes as tipping accelerators, (iii) AI-driven ESDTs, and (iv) anticipatory governance frameworks. At least five tipping elements—including the Greenland Ice Sheet, West Antarctic Ice Sheet, and tropical coral reefs—approach critical thresholds within the Paris Agreement target range, with compound extremes amplifying risks nonlinearly. Current ESDTs lack explicit tipping cascade modules, interpretability standards, and equitable access. We introduce CASCADE-AI (Cascading Assessment of Systemic Climate Abrupt Dynamics through Artificial Intelligence), linking tipping cascade characterization, compound extreme assessment, physics-AI hybrid digital twin architecture, and governance interfaces. Two critical integration gaps are identified: between compound extreme attribution and tipping characterization, and between ESDT outputs and governance application. Ten prioritized research actions are proposed across near (2025–2028), medium (2028–2032), and long-term (2032–2040+) horizons.
Climate-Smart Agriculture and Carbon Emissions: Panel Data Evidence on Policy Effectiveness in Sub-Saharan Africa (2000-2024)
(Sciencedomain International, 2026-05-08) Makieu, Peter
Agriculture in Sub-Saharan Africa accounts for approximately 59 percent of total national greenhouse gas emissions- the highest proportional share globally- and these emissions grew by 35 percent between 2000 and 2021. This study investigates whether climate-smart agriculture policies have resulted in a reduction in per-capita carbon dioxide (CO2) emissions across 15 Sub-Saharan African countries from 2000 to 2024, with a particular focus on the period following the 2015 Paris Agreement. A balanced panel dataset of 375 country-year observations was constructed from the World Bank Development Indicators, the Food and Agriculture Organization statistical database (FAOSTAT), the International Monetary Fund, and the NASA POWER climate database. Pooled ordinary least squares, fixed effects, and random effects panel models were estimated. The Hausman specification test confirmed the fixed effects estimator as preferred. All models were corrected for confirmed heteroskedasticity and serial autocorrelation using country-clustered robust standard errors. Panel cointegration tests confirmed a long-run equilibrium structure. The primary finding is that agricultural productivity- the climate-smart agriculture proxy- exerts a significant negative effect on per-capita CO2 emissions. The fixed effects elasticity of -2.236 indicates that a 10 percent improvement in agricultural productivity is associated with approximately 22 percent lower per-capita emissions, controlling for temperature, rainfall, and agricultural land expansion, which increases them. A non-linear income-emission relationship was confirmed, consistent with a modified Environmental Kuznets Curve pattern. The climate-smart agriculture CO2correlation strengthened considerably after 2015, suggesting that nationally determined contribution commitments are beginning to generate measurable emission outcomes. Policy simulations indicate that scaling climate-smart agriculture investment to recommended agricultural budget targets could achieve aggregate emission reductions of 12-18 percent by 2030.
Digital Transformation and Crop Production in Sub-Saharan Africa: Evidence from Dynamic Panel Data Analysis
(Sciencedomain International, 2026-09-30) Makieu, Peter
Digital transformation can reduce information frictions and expand access to markets, extension services, and financial tools, but its relationship with agricultural outcomes in Sub-Saharan Africa (SSA) remains sensitive to measurement and econometric specification. This study examines the association between a connectivity-based Digital Transformation Index (DTI) and crop production in 40 SSA countries over 2000-2022 (N = 769 country-year observations). The DTI combines mobile-cellular subscriptions, internet use, and fixed-broadband subscriptions, while the outcome is the natural logarithm of the FAO Crop Production Index (2014-2016 = 100), an aggregate production index rather than an input-normalised productivity measure. Entity fixed effects (FE) provide the main static estimate, while pooled OLS, random effects, two-way FE, System GMM, Difference GMM, component-level models, income-group estimates, and a quadratic specification are used for sensitivity analysis. Entity FE yields a positive DTI coefficient (beta = 0.948, p < 0.001), whereas the estimate is smaller and only marginally significant with year effects (beta = 0.523, p = 0.095). In the dynamic models, System GMM produces a positive but imprecise DTI estimate (beta = 0.031, p = 0.409), while Difference GMM yields a negative and imprecise estimate (beta = -0.059, p = 0.581). Separate income-group FE models show a larger coefficient in low-income countries (beta = 1.841) than in lower-middle- and upper-middle-income groups (approximately 0.48), although no formal cross-group coefficient-equality test is reported. The quadratic FE specification is concave and implies a sample-specific turning point near DTI = 0.709, located in the upper tail of the observed distribution. Overall, the results support a positive long-horizon within-country association in the static FE model, but the weaker time-controlled and dynamic estimates warrant cautious, non-causal interpretation.
Evaluating the Efficiency and Performance of Minimalist Neural Network Architectures with Hybrid Activation Functions
(2025-10-23) Makieu, Peter
This research introduces an Adaptive Hybrid Activation Function (AHAF) for minimalist neural networks, addressing efficiency and performance challenges in resource-constrained applications.Unlike existing hybrid activations (e.g., Swish) or minimalist approaches (e.g., Lottery Ticket Hypothesis (LTH)), AHAF incorporates dynamic α-adaptation (0.50-2.13 range) with theoretical guarantees, achieving O(1/√T) convergence (Theorem 1) and gradient saturation mitigation (Lemma 2).Evaluated on Iris and Modified National Institute of Standards and Technology (MNIST) datasets, 8-unit AHAF networks matched 128-unit Rectified Linear Unit (ReLU) models in accuracy (96.67%) while reducing parameters by 84% and improving convergence speed by 18-22%.AHAF demonstrated superior adversarial robustness under Projected Gradient Descent (PGD) attacks (ϵ=0.1),showing only 1.18% accuracy drop versus ReLU's 3.42% (p 1.0).These results challenge conventional assumptions about hybrid activation overhead, establishing AHAF as a framework for sustainable Artificial Intelligence (AI) deployment in edge computing and adversarial-prone environments.The study bridges two key paradigms -dynamic activation adaptation and minimalist architecture design -offering both theoretical advances (formalized convergence proofs) and empirical validation (energy efficiency metrics, adversarial testing).By demonstrating that small networks with meta-learned activations can outperform traditional architectures, this work provides actionable insights for developing efficient, robust AI systems without sacrificing accuracy.The replicable methodology and open benchmarks further support adoption in real-world applications where computational resources and model stability are critical constraints.
Financial Development, Foreign Direct Investment, and Private Sector Development in Sierra Leone: Evidence from ARDL and Granger Causality Analysis (1996–2024)
(Springer Science and Business Media LLC, 2026-07-30) Makieu, Peter
This study examines the relationships among financial development, foreign direct investment (FDI), and private sector development in Sierra Leone over the period 1996–2024, using annual data from the World Bank, World Development Indicators (WDI) and the Worldwide Governance Indicators (WGI). An autoregressive distributed lag (ARDL) framework is employed alongside moderation analysis and Granger causality tests to investigate long-run and short-run dynamics. The analysis finds that financial development and institutional quality — measured by government effectiveness, regulatory quality, and rule of law — are positively and significantly associated with private sector development in the long run. Foreign direct investment(FDI) exhibits a positive and significant association in the model incorporating government effectiveness, though results are mixed across institutional specifications. The moderation analysis indicates that institutional quality conditions the relationship between foreign direct investment (FDI) and private sector outcomes, with government effectiveness exerting the strongest moderating effect. The error-correction term is negative and significant at the 10% level, consistent with long-run equilibrium adjustment. Granger causality results reveal a unidirectional causal flow from foreign direct investment(FDI) to private sector development but bidirectional causality between financial development and private sector outcomes. The findings contribute to the literature on the foreign direct investment(FDI) –finance–institution nexus in sub-Saharan Africa and carry implications for financial sector deepening and institutional reform in Sierra Leone.
Foreign Direct Investment and Private Sector Development in Sierra Leone: Examining the Moderating Roles of Financial Development and Institutional Quality
(Springer Science and Business Media LLC, 2026-09-21) Makieu, Peter
This study examines the long-run and short-run relationship between foreign direct investment (FDI) and private sector development in Sierra Leone for the period 1996–2023. The dependent variable is domestic credit to the private sector as a share of gross domestic product (GDP), which is considered as a proxy for private sector development. The study examines the moderating effect of institutional quality, proxied by a composite index of World Governance Indicators. This index is on the effect of FDI on private sector development. Annual data from the World Bank World Development Indicators and World Governance Indicators are used to conduct the analysis using the autoregressive distributed lag (ARDL) bounds testing framework. Robustness estimators are fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) and short run dynamics are captured by an error correction model (ECM). The autoregressive distributed lag (ARDL) bounds F-statistic of 18.82 confirms the cointegration. Long-run estimates indicate that foreign direct investment (FDI) has a positive and significant impact on private sector development across all estimators. The interaction term between foreign direct investment (FDI) and institutional quality is positive and significant in all specifications. This suggests that improvements in governance enhance foreign direct investment's contribution to private sector development. The error correction term of − 0.346 (P = 0.01) implies that deviations from long-run equilibrium are corrected at 34.6% per annum. Results are robust to the exclusion of the Ebola shock period and to the decomposition of the governance index into its individual governance pillars. The results suggest that investment promotion and institutional reform are complementary policy priorities in post-conflict economies.
Microplastics as Microbial Hotspots for Antibiotic Resistance Gene Dissemination in Agricultural Soils: A Critical Appraisal of Mechanisms and Environmental Drivers
(Sciencedomain International, 2026-09-19) Makieu, Peter
Plastic residues have become a persistent structural component of arable land, and the microbial films that develop on their surfaces have been proposed as concentrated sites for the accumulation and exchange of antibiotic resistance genes. This review critically examines the evidence that microplastics act as hotspots for antibiotic resistance gene dissemination in agricultural soils, the mechanisms invoked to explain that behaviour, and the environmental and agronomic conditions that modulate it. Literature was identified through structured searching of open bibliographic and scholarly indexes, supplemented by backward and forward citation tracking and by checks for corrections and retractions, with priority given to soil-based experimental and field studies, mechanistic work on gene transfer, and multi-site surveys. The evidence consistently shows that plastic surfaces recruited from soil support communities that differ compositionally from the surrounding matrix and that carry a higher relative abundance of resistance determinants and mobile genetic elements. Confidence in the stronger interpretation, that plastic itself selects for resistance, is weaker. Comparisons are most often drawn between a colonised surface and bulk soil rather than against inert or natural particulate controls, and the single study identified that included wood and glass comparators found that a natural substrate enriched resistant organisms to a similar degree as polystyrene. Mechanistic support is strongest for physical proximity within biofilms, for host community reassembly towards taxa that already carry multidrug determinants, and for stress-mediated stimulation of conjugative transfer through reactive oxygen species and altered membrane permeability. Biodegradable polymers, rather than conventional ones, produce the largest and most reproducible increases in soil resistance gene abundance, which complicates their promotion as a straightforward substitute. Manure and long-term organic fertilisation, metal and biocide co-contamination, particle size, ageing and soil reaction emerge as the principal modulators. Translation from gene abundance to demonstrated resistance transfer into pathogens under field conditions remains unresolved, and quantitative risk statements should be treated as provisional.
Nutritional values of cassava leaves in three districts, Kenema, Kailahun, and Bo, Sierra Leone
(Elsevier BV, 2025-04-01) Makieu, Peter
Prospects and Consumer Perception of Sustainable Protein Innovations for Food Security in Sierra Leone
(Sciencedomain International, 2025-11-26) Makieu, Peter
**Introduction:** Sierra Leone experiences major food security problems that repeatedly occur, and malnutrition is one of the biggest problems for children, women, and rural poor groups. The country, which has been dependent on traditional animal proteins for a long time, is finding this source less and less feasible due to economic difficulties, unstable supply chains, and has been affected by climate change, due to reduced productivity. At the same time, as the need for environmentally friendly protein sources becomes high (to reduce environmental damage and solve the problem of hunger), plant-based foods, edible insects, and aquaculture-sustainable protein innovations (SPIs) are the ‍‌‍‍‌solutions. **Aims:** The study aims to assess Sierra Leonean consumers’ readiness to accept SPIs (via urban-rural awareness, perceptions, and adoption drivers) and identify political-economic leverage points for integrating SPIs into local and national food security strategies. Study Design: This study was based on mixed methods, involving household surveys for the quantitative aspect and interviews with stakeholders for the qualitative aspect. Place and Duration of Study: The data collection period was from January to May 2025, and it covered five agro-ecological zones in Sierra Leone, i.e., Freetown (urban), Bo, Kenema, Makeni (semi-urban), and Moyamba (rural), which combined represent 46% of the total population. **Methodology:** Through stratified random sampling, 500 household heads (≥18 years old) were surveyed, and 40 key informants (nutritionists, policymakers, and food industry actors) were selected for interviews. Quantitative data were handled using SPSS (descriptive statistics, logistic regression), and qualitative data were analyzed through thematic analysis. **Results:** Out of the total respondents, only 28.6% had heard of SPIs, where the urban Freetown had the highest rate of awareness (41%), and the rural Moyamba had the lowest (16%). Besides that, nutrition (M = 3.9/5) and environmental benefits (M = 3.7/5) were the most influential factors that shaped people's attitudes, whereas taste aversion was the biggest barrier to turn that point of view into action by increasing from 37% in Freetown to 63% in Moyamba. As education (OR = 1.57, p < 0.001) and prior knowledge (OR = 1.63, p < 0.001) were indicated by logistic regression most significant adoption factors. In addition, the drop to under 30% of the willingness to adopt when the SPI's price was more than 1.2 times that of animal-source substitutes indicates that they were price-sensitive. **Conclusion:** The main reasons for people in Sierra Leone to choose healthy and environmentally friendly products are health and sustainability; however, due to the low level of awareness, sensory skepticism, and price sensitivity, the uptake of SPIs is limited. Firstly, the policy should focus on sensory urban-first demonstrations to attract the target consumers for awareness; secondly, culturally-tailored product development, and thirdly, targeted subsidies to reduce price barriers. In addition, facilitating SPI learning through the education system and cooking extension programs for women are not only effective in speeding up the adoption process but also in building climate-resilient food systems.
Synergistic Impacts of Microplastic Pollution and Climate Extremes on Crop Productivity: Multi-Omics Insights and CRISPR/Cas9-Mediated Mitigation Strategies
(Sciencedomain International, 2026-05-08) Makieu, Peter
Convergent environmental pressures, including accelerating climate extremes and pervasive microplastic contamination of agricultural soils, pose compounding threats to global crop productivity that remain poorly characterized at the mechanistic level. In this PRISMA-compliant systematic review of 142 peer-reviewed studies (2015–2026), we integrate multi-omics evidence (transcriptomics, proteomics, and metabolomics) to elucidate molecular reprogramming underlying the combined effects of drought, heat, and microplastic stress in major crops. Polyethylene microplastics reduce soil water-holding capacity and increase ammonia volatilization by up to 33.9%, while nanoplastic uptake induces reactive oxygen species accumulation and lipid peroxidation. Key transcription factor families, including DREB, NAC, and MYB, emerge as central regulatory hubs under dual stress. We propose a tripartite mitigation framework that (i) leverages CRISPR/Cas9-mediated editing of stress-responsive loci (OST2, SAPK2, ARGOS8, BnaA9.NF-YA7) for intrinsic resilience; (ii) implements IoT-enabled precision irrigation and hyperspectral monitoring for real-time stress management; and (iii) employs biochar-assisted physical sorption and synthetic microbial communities to restore soil health. This integrative synthesis provides actionable guidance for experimental validation, identifies priority CRISPR targets, and highlights knowledge gaps in long-term microplastic degradation and stress interaction, offering a mechanistic and translational roadmap for developing climate- and microplastic-resilient crops.
TRANSFORMATIVE IMPACTS OF AI ON DIGITAL MEDIA: AN ANALYSIS OF INDUSTRY-SPECIFIC TRENDS, ECONOMIC OUTCOMES, AND CONSUMER TRUST (2020-2025)
(2025-09-30) Makieu, Peter
The rapid evolution of artificial intelligence (AI) is reshaping the digital media landscape, prompting critical inquiries into its industry-specific impacts and implications for economic outcomes and consumer trust. This study presents a comprehensive analysis of AI adoption trends from 2020 to 2025, leveraging a mixed-methods approach that incorporates a Kaggle-sourced dataset with 1,240 industry-year observations.Our findings indicate substantial disparities in AI adoption rates, with the U.S. automotive sector leading at 81.06, while South Korean healthcare remains at a mere 10.53. High-adoption sectors experience remarkable revenue growth of 45.6–57.86, despite concurrent job losses ranging from 10.66 to 27.62. Employing sophisticated regression analyses, we identify infrastructure investment as a significant predictor of AI adoption (0.58, p < 0.01), contrasting with the detrimental effects of regulatory strictness (0.34, p < 0.05).A novel contribution of this research is the exploration of consumer trust dynamics, which inversely correlates with AI adoption rates (-0.12). This study reveals that trust levels vary significantly across sectors, with marketing achieving a trust rate of 81.58% compared to only 41.77 in gaming, largely driven by transparency tool adoption.In conclusion, we propose an innovative policy framework that aims to align AI-driven growth with ethical considerations, emphasizing the role of transparency and sector-specific governance. Our research enriches the understanding of technology adoption models and offers actionable insights for policymakers and industry leaders, facilitating sustainable innovation in the digital media sector. This study not only fills critical gaps in existing literature but also sets the stage for future research on the intersection of AI and digital media.