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Background: This study examines the application of Artificial Intelligence (AI) and Big Data Analytics (BDA) in enhancing humanitarian supply chain resilience, focusing on Ghana and South Africa. Despite their potential, AI-BDA applications are underexplored in disaster response, particularly in developing economies. Methods: An explanatory research design using a quantitative approach was employed, analyzing data from 200 supply chain professionals in both nations. Structured questionnaires assessed the implementation of four key AI-BDA techniques: Time-Series Forecasting (TSF), Early Warning Systems (EWS), Logistics Optimization (LO), and Real-time Monitoring (RTM). Exploratory factor analysis and regression analysis were conducted to evaluate the relationship between these techniques and supply chain resilience, controlling for organizational size and technological readiness. Results: The findings indicate that AI-BDA techniques significantly improve humanitarian supply chain resilience, with TSF and LO demonstrating the highest predictive power. Additionally, technological readiness facilitates the adoption of these techniques. Conclusions: While AI-BDA offers substantial benefits, opportunities for greater adoption remain, particularly in real-time monitoring and predictive analytics. Humanitarian organizations should invest in capacity-building initiatives, enhance data quality, and foster multi-stakeholder partnerships to maximize the impact of AI-BDA.

Solar water heating systems (SWHS) offer a sustainable solution for reducing reliance on conventional energy sources; however, their performance is often limited by insufficient heat transfer within the collector. This study presents a CFD-based numerical investigation on the optimization of finned thermal collectors in a solar water heating system using Al2O3/water hybrid nanofluid. The effects of nanoparticle volume fraction (1–3%), fin geometry (triangular and hexagonal), and mass flow rate (5–20 kg/h) on the thermal and heat transfer performance of the system were analyzed. Key performance indicators including absorber/PV temperature, outlet fluid temperature, convective heat transfer coefficient, thermal efficiency, and improved daily efficiency were evaluated under transient operating conditions. The results show that increasing Al2O3 concentration enhances heat transfer and thermal efficiency due to improved thermophysical properties of the working fluid. Fin geometry significantly influences thermal behavior, with hexagonal fins generally producing higher outlet temperatures and thermal efficiency of 65%, while triangular fins provide higher daily efficiency improvement under optimized conditions. The convective heat transfer coefficient increased with both nanoparticle concentration and flow rate, reaching peak values during mid-day hours corresponding to maximum solar input. The study confirms that combining optimized fin structures with Al2O3/water nanofluids provides an effective strategy for improving the thermal performance of solar water heating collectors, while CFD modelling offers a reliable approach for system design and performance prediction.

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from 8/ton to12/ton. Firewood generates health savings and carbon revenue in the range of 4100– 12,270/year. Prices below 8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints.

Energy-intensive processes such as flexographic printing, extrusion coating, slitting, compressed air generation, and chilled water production make liquid carton packaging manufacturing a major electricity consumer, increasing the need for cost-effective and sustainable energy solutions. This study evaluates the real-world performance of a 679 kWp grid-tied solar photovoltaic (PV) system integrated at the 11 kV level in a liquid carton packaging factory in Nairobi, Kenya, operating under regulatory export control constraints that require full on-site consumption of PV generation. Using measured operational data from energy monitoring platforms, including Sunny Portal, 1.31.8 Schneider EcoStruxure, and Sphera Cloud 8.17.2, system performance was assessed in accordance with IEC 61724-1, focusing on final yield, capacity utilization factor, grid offset contribution, and carbon emissions reduction. The results show that the system generated 617 MWh over the assessment period, corresponding to an average daily final yield of 2.49 kWh/kWp·day and a capacity utilization factor of 10.38%. On-site PV generation supplied approximately 17% of the plant’s annual electricity demand and avoided about 277.7 t CO2 emissions. Performance benchmarking against comparable installations in Kenya, Morocco, Malaysia, Senegal, and Uzbekistan indicates that the lower observed yield is primarily driven by curtailment and industrial load-matching limitations rather than inadequate solar resource or component inefficiency. The findings demonstrate that meaningful electricity cost savings and emissions reductions can be achieved in energy-intensive manufacturing environments despite export restrictions while highlighting the importance of improved load alignment and data-driven operational strategies to enhance PV utilization.

This work develops an inverse data envelopment analysis (Inverse DEA) framework for portfolio optimization, treating return as a desirable output and volatility as an undesirable output. Using 20 industry-level portfolios from the Taiwan Stock Exchange (1365 stocks; FY-2020), we first evaluate efficiency with a directional-distance DEA model and identify 7 inefficient industries. We then formulate an Inverse DEA model that holds inputs and desirable outputs fixed and estimates the maximum feasible reduction in volatility. Estimated reductions range from 0.000827 to 0.007610, and substituting these targets into the base model drives each portfolio’s inefficiency score to zero (ϕ=0), thereby making them efficient. To test robustness, we extend the analysis to a calm pre-crisis year (2019) and a recovery year (2021), which confirm that inefficiency and volatility-reduction targets behave logically across regimes, smaller cuts in stable markets, larger cuts in stressed conditions, and intermediate adjustments during recovery. We interpret these targets as theoretical envelopes that inform risk-reduction priorities rather than investable guarantees. The approach adds a forward-planning layer to DEA-based performance evaluation and provides portfolio managers with quantitative, regime-sensitive volatility-reduction targets at the industry level.

The cement industry accounts for nearly 8% of global anthropogenic CO2 emissions, driven largely by energy-intensive clinker production. Valorising industrial and agricultural waste as Supplementary Cementitious Materials (SCMs) presents a viable mitigation strategy, aligning decarbonisation goals with circular-economy principles. This review employs a two-stage screening process and the Evaluation based on Distance from Average Solution (EDAS) method to assess 27 SCMs across technical, environmental, economic, and regulatory dimensions. The results establish a clear hierarchy: fly ash and metakaolin ranked highest, followed by ground granulated blast furnace slag, silica fume, and calcined clay. Life cycle assessment confirms these top-performing SCMs can reduce the global warming potential of cement production by 50–90% compared to ordinary Portland cement. While established SCMs like fly ash offer a balanced profile in durability, CO2 reduction, and cost, the framework also identifies regionally abundant materials such as steel slag, bagasse ash, red mud, and Rice Husk Ash (RHA), which possess significant potential but require further processing and standardisation. The findings underscore that material consistency, robust regional supply chains, and performance-based standards are critical for large-scale SCM adoption, providing a replicable framework to guide industry and policy stakeholders in accelerating the transition to low-carbon, waste-valorised cement technologies.

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations.

The global economy faces increasing environmental challenges and economic instability, prompting the adoption of innovative energy technologies as a crucial strategy. This study addresses the urgent quest for sustainable development in South Africa, specifically by evaluating renewable energy solutions. This study utilizes a comprehensive literature analysis to examine the current state of renewable energy infrastructure, policy frameworks, technological advancements, and economic viability within the South African context. Synthesizing insights from the existing literature on the interplay between energy, economy, and technology, this study aims to provide a refined understanding of renewable energy solutions’ feasibility and integration potential. The exploration of these solutions in South Africa identifies key opportunities, challenges, and implications for sustainable development. These findings offer valuable guidance for policymakers, researchers, and stakeholders in advancing a country’s transition towards a sustainable energy future.

Polypyrrole-based functional composites are increasingly explored and extensively adopted for energy storage, sensing, and environmental applications due to their tunable electronic properties, chemical versatility, and mechanical stability. However, rational optimization of these composites requires a unified understanding of electronic, mechanical, thermal, and chemical behavior at the atomic scale, which underlies their multifunctional behavior, and remains fragmented. Notably, Density Functional Theory (DFT) provides indispensable atomistic insight into the electronic, mechanical, thermal, and chemical interactions that govern the performance of multifunctional materials. To bridge these gaps, this review presents a comprehensive assessment of recent DFT and time-dependent DFT (TD-DFT) studies that elucidate the electronic, mechanical, thermal, and chemical characteristics of polypyrrole and its hybrid composites. Key theoretical descriptors, including electronic structure modulation, charge transfer behavior, adsorption energetics, interfacial binding energies, hydrogen bond formation, and charge redistribution, are critically assessed to establish structure–property relationships across diverse functional systems. Considerable attention is given to interfacial interactions, doping strategies, and composite architectures that govern durability, conductivity, and chemical stability. By consolidating current atomistic insights and identifying existing limitations, this review provides a coherent framework for rational material design. Notably, it presents the first systematic quantification of dopant steric effects in PPy multifunctional composites, linking atomistic-scale modifications to the optimization of functional properties in next-generation applications.

Several environmental impacts are associated with cement production, ranging from high greenhouse gas (GHG) levels to high energy consumption (fossil fuel and electricity) to high resource usage. Due to the growing demand for cement in the industry and limited studies in South Africa, it is essential to evaluate the environmental impact of cement production in the South African context. In this study, an analysis of the production model of South African (SA) cement plants was carried out to quantify its impacts and decipher how they consequently affect lives, resources, and the ecosystem. This study carried out a Life Cycle Assessment (LCA) of cement using both the mid-point and end-point approaches of the Life Cycle Impact Assessment (LCIA). This study carried out a cradle-to-gate analysis of 1 kg of cement produced in a typical SA plant. The result showed that for every 1 kg of cement produced, 0.993 CO2 eq was emitted into the atmosphere; 98.8% was actual CO₂ emission, and its resultant effect was global warming, which causes changes in climatic conditions. Also, 1.6 kg of 1,4-Dichlorobenzene (1,4-DCB) eq was emitted into the air and water, which caused high toxicity in these media, and for every 1 kg of cement produced, 0.139 kg of oil eq was produced, and its effect was seen in fossil resources’ scarcity. The end-point result showed that 55,404 was the potential number of human lives that could be endangered annually; 133 species had the potential to be endangered annually, and the effect of a potential scarcity of resources caused a total marginal price increase of ZAR 6.2 billion due to these damages. In conclusion, this study prescribed mitigation and adaptation strategies to counter these environmental impacts.