Journals
2026 EN
Fatimata Belem Wendimi · Sangare Ibrahim · Gilbert Caroline
+1 more
ABSTRACT Dengue fever is an arbovirus disease caused by the dengue virus and has been diagnosed in Burkina Faso for many years. In recent decades, the disease has become a growing concern, thereby impacting the public health system. Several factors contribute to the pathogenesis of dengue fever, including the immune system and the virulence of different serotypes. Additionally, multiple complex conditions, including the spread of the Aedes mosquito vector and meteorological factors, contribute to the disease's spread. Therefore, effective disease management must be comprehensive, involving strategic combinations including community engagement, mosquito control, and public health measures. This approach has been implemented in Burkina Faso, with some success. Although several studies have focused on viral control, the isolation of virus serotypes, the prevalence and seroprevalence of the disease in specific populations, information on the overall burden of dengue fever is scarce in the country, as it presents classic symptoms similar to those of malaria and some arbovirus diseases encountered in the country. However, limited access to diagnostic tools, an inadequate surveillance system, a lack of awareness among healthcare workers, auto‐medication, and ongoing conflicts in the country may lead to an underestimation of its burden and a limited understanding of its epidemiology. Here, we discuss dengue fever and the factors associated with the underestimation of its burden in Burkina Faso, drawing on government documents and published data. This review aims to describe the impact of managing this neglected tropical disease, advocating for improved surveillance and control efforts in the country.
Journals
2026 EN
Erüst Ali Can · Taşcıkaraoğlu Fatma Yıldız · Küçükdemiral İbrahim Beklan
ABSTRACT This paper addresses the trajectory tracking problem for a full‐state quadrotor subject to physical model constraints and unknown external disturbances. A robust tube‐based model predictive control (MPC) approach is successfully applied to the system, which is subject to bounded disturbances and hard constraints. In the literature, to reduce the computational complexity of standard time‐triggered (ET) MPC without sacrificing performance, ET‐MPC has been proposed, solving the optimal control problem only when an event is triggered. In this study, a dynamic threshold set is determined based on the worst‐case disturbance effect and the deviation between the actual and predicted states of the quadrotor. Additionally, the discrete‐time model of the quadrotor is extended with integral action, enabling the quadrotor to track the reference trajectory without error. To demonstrate the effectiveness of the proposed method, simulation results for time‐triggered tube MPC and tube‐based dynamic ET‐MPC are compared. The proposed method proves its computational efficiency without compromising trajectory tracking performance. Moreover, the dynamic event trigger reduces the computational load 65%–85%, with an acceptable level of control performance degradation.
Journals
2026 EN
Ibrahim Abdelazim · Zayed Tarek · Lafhaj Zoubeir
ABSTRACT Megaprojects, characterized by immense scale and complexity, frequently suffer from inefficiencies, cost overruns, and delays. Lean Construction (LC) offers a strategic solution to enhance efficiency and sustainability, yet its adoption lacks empirically validated, context‐specific frameworks, particularly in rapidly developing economies like China. This study bridges this gap by developing and validating a comprehensive model linking lean construction practices (LCPs) to LC adoption (LCA) in megaprojects. Through a mixed‐methods approach, literature review, expert interviews, and a survey of 379 professionals, 35 LCPs were identified and categorized into six constructs. Structural equation modeling (SEM) confirmed that all six LCP categories significantly and positively influence LCA ( p < 0.001). People Involvement and Continuous Improvement emerged as the strongest predictor ( β = 0.241), followed by safety and quality assurance ( β = 0.202), customer focus and waste elimination ( β = 0.195), standardization and process transparency ( β = 0.192), flow and pull systems ( β = 0.130), and planning and scheduling ( β = 0.110). Subsequently, fuzzy synthetic evaluation (FSE) prioritized “Planning and Scheduling” as the highest‐impact practice, followed by “Flow and Pull Systems” and “Customer Focus and Waste Elimination.” This dual‐method validation not only confirms the theoretical model but also provides a practical, prioritized roadmap for policymakers and practitioners. The findings offer actionable strategies to reduce waste, enhance collaboration, and improve sustainability, making a critical contribution to resilient global infrastructure development.
Journals
2026 EN
Lyu Zhenlei · Lanre Ibrahim Ridwan · Alomair Abdulrahman
+1 more
ABSTRACT This study investigates the asymmetric health impacts of fossil fuel dependence in Sub‐Saharan Africa (SSA) using the method of moments quantile regression (MMQR), Driscoll–Kraay robustness checks, and interaction models with clean cooking energy (CCE). Unlike conventional analyses focused on mortality, we examine chronic outcomes—respiratory illness, disability, and out‐of‐pocket health expenditure—across 48 SSA countries from 1990 to 2022. Results show that oil, gas, and coal consumption significantly increase health and financial burdens, with stronger effects in upper quantiles, indicating disproportionate harm among already vulnerable populations. Public health expenditure and CCE consistently mitigate these effects, while Granger quantile causality confirms fossil shocks precede worsening outcomes. Findings highlight a persistent “fossil fuel–health trap” in SSA that undermines SDGs 3 (health), 7 (clean energy), and 13 (climate action). Expanding access to clean energy and strengthening health systems are critical to reducing inequities and supporting sustainable development.
Journals
2026 EN
Yue Xinting · Lanre Ibrahim Ridwan · Alomair Abdulrahman
+1 more
ABSTRACT Why do resource‐rich African countries struggle to achieve inclusive and sustainable development despite decades of extractive wealth? This study revisits the resource curse hypothesis through the lens of institutional mediation and SDG‐aligned outcomes, focusing on 10 of Africa's most resource‐dependent economies between 1996 and 2022. Employing a mixed‐method econometric strategy—Common Correlated Effects Mean Group for baseline analysis, Cross‐Sectionally Augmented ARDL (CS‐ARDL) for short‐run, and Mean Group estimators for country‐specific insights—the study investigates the effects of natural resource rents on economic growth (SDG 8), income inequality (SDG 10), environmental degradation (SDG 13), and militarization (SDG 16). A composite institutional quality index, derived via Principal Component Analysis of six World Governance Indicators, is introduced as a moderator to capture governance effectiveness. Results reveal a consistent pattern of multidimensional resource curse: resource rents depress growth, worsen inequality, heighten environmental degradation, and drive up military spending. However, strong institutional quality systematically mitigates these negative outcomes. Country‐specific estimates identify Nigeria, Angola, and Congo as particularly vulnerable to institutional failure. These findings affirm that institutional capacity is not just a mediating variable but a decisive condition for converting extractive wealth into sustainable development. The study offers actionable policy insights aligned with SDG targets, emphasizing the need for governance reform, fiscal accountability, and environmental responsibility. It contributes to a growing literature that calls for disaggregated, multidimensional, and governance‐sensitive approaches to resource management in Africa.
Journals
2026 EN
Chiew Shing Mei · Ibrahim Izni Syahrizal · Tang Lee Chong
+3 more
Abstract The depletion of natural sand and the limited investigation into manufactured sand (M‐sand) as fine aggregate replacement in high‐strength concrete (HSC) present key challenges for sustainable construction. This study investigates the effects of varying levels of M‐sand replacement (10%–100%) on the fresh and mechanical properties of HSC. Experimental testing was conducted to evaluate its workability, compressive strength, and microstructural characteristics. A total of 278 datasets, including both literature‐derived and experimental data, were used to develop predictive models employing artificial neural networks (ANN) and regression trees (RT). The ANN and RT models were trained and validated through blind testing, with the ANN demonstrating superior predictive accuracy within a ±20% error range. Experimental results showed that a complete replacement of natural sand with M‐sand led to a 25%–35% increase in compressive strength, attributed to improved particle packing and matrix densification. Microstructural analysis confirmed the reduction in interfacial voids at higher M‐sand replacement levels. The findings suggest that M‐sand is a viable and sustainable alternative to natural sand in HSC applications. Furthermore, the ANN model offers a reliable and efficient tool for predicting compressive strength, potentially reducing the need for extensive physical testing. These outcomes contribute to sustainable construction practices and provide practical insight into the application of machine learning for concrete mix optimization.
WILEY‐VCH Verlag GmbH & Co. KGaA
Journals
2026 EN
Chandran Chinchila · Mohan Manoj · Dawi Elmuez
+5 more
Due to its zero carbon emissions, hydrogen has emerged as a promising clean energy source. By utilizing water electrolysis for hydrogen production, carbon neutralization can be advanced technologically and practically. Developing durable, cost‐effective electrocatalysts with low overpotentials is essential for electrochemical water splitting. In order to produce hydrogen efficiently, it is important to choose materials that are most suitable for converting energy into hydrogen. Due to their tunable structure, expansive surface area, and outstanding electrocatalytic properties, carbon nanomaterials are becoming increasingly important in this field. Furthermore, their high conductivity and catalytic potential make them promising hydrogen energy candidates. As a precursor material, biochar can be used to produce carbon nanomaterials in an innovative manner. Carbon nanomaterials have been synthesized from biochar in a variety of ways, each producing a different structure. This review discusses biochar production and biochar nanostructures derived from biochar, including carbon dots, carbon tubes, nanofibers, nanosheets, and nanoflakes, along with their energy conversion efficiency and structural tunability. Furthermore, this review investigates recent advances in electrochemical water splitting. It places a particular emphasis on carbon nanomaterials derived from biochar as catalysts. Its objective is to provide valuable insight into the advancement of sustainable hydrogen energy solutions.
Journals
2026 EN
Ghaffar Abdul · Qaisar Muhammad Ahsan Farooq · Liu Jun
+9 more
The rising global energy demand requires the development of high‐performance supercapacitors (SCs) that synergize high‐power density with substantial energy density. The pursuit of such energy storage devices is fundamentally related to the innovation of advanced electrode materials. Two‐dimensional graphitic carbon nitride (g‐C 3 N 4 ) has recently emerged as a compelling candidate, distinguished by its unique nitrogen‐rich structure, tunable electronic properties, and facile synthesis. This review provides a comprehensive and critical investigation of g‐C 3 N 4 ‐based materials for SCs. We systematically analyze the crystal structure, physicochemical properties, and synthesis methodologies of g‐C 3 N 4 , correlating these characteristics with their electrochemical performance. For the first time, a detailed comparative analysis is presented, categorizing strategies into the engineering of pristine g‐C 3 N 4 , heteroatom doping, and the construction of composites. We place particular emphasis on the superior performance of composites formed with conductive polymers, transition metal oxides/sulfides (TMOs/TMSs), graphene, MXenes, and other families, where synergistic effects enhance conductivity, stability, and charge storage capacity. Finally, we provide a critical outlook on the existing challenges and future possible directions, aiming to guide the rational design of next‐generation g‐C 3 N 4 ‐based electrode materials to unlock their full potential in SCs.
Journals
2026 EN
Parker Kyle · Wang Zhen · Rauf Yahya
+5 more
Abstract The first hexaploid bread wheat reference genome from Chinese Spring was released in 2018 by the International Wheat Genome Sequencing Consortium and is considered as the industry standard reference. To explore the effects of different reference genomes on variant discovery, 29 hexaploid bread wheat ( Triticum aestivum L.) cultivars from the Southern Great Plains of the United States with varying whole genome sequencing depth were aligned to three reference genomes. The reference genomes varied in evolutionary similarity to the cohort of the germplasm analyzed: (1) Jagger, a Kansas State University cultivar, a reference with high similarity, (2) Chinese Spring, a Chinese landrace, the current industry standard, (3) Durum‐ tauschii , an “in silico” hybridization of the reference genomes of tetraploid durum wheat and wild Aegilops tauschii , for an unrepresentative reference. The Jagger reference genome retained more informative variants after filtering. Synteny regions and large introgressions were identified by read alignment coverage, using diverse reference genomes to identify lines containing Aegilops ventricosa (2N v S), 1AL:1RS and 1BL:1RS ( Secale cereale ) rye translocations, and a 2.8 Mb contig from TAM 112 known to contain a haplotype harboring greenbug [ Schizaphis graminum (Rondani)] resistance gene Gb3 . The choice of reference genome is important, but any single reference can induce bias. This study with US Southern Great Plains germplasm demonstrated the importance of multiple reference genomes to capture genetic diversity.
Journals
2026 EN
Adewale Samuel A. · Babar Md Ali · Jarquin Diego
+12 more
Abstract Genomic selection (GS) is a promising strategy for accelerating genetic gains of complex traits in breeding programs. Despite the recent advancements in high‐throughput genotyping technologies, the selection of the type of marker systems needed for GS remains challenging in breeding programs. In this study, we explored 3K array single nucleotide polymorphisms (SNPs) and genotyping by sequencing (GBS) SNP markers for genomic prediction of oat biomass yield using different statistical and machine learning approaches. An oat panel consisting of 420 lines was phenotyped for biomass‐related traits for 3 years and genotyped using two different marker platforms (3K array and GBS). Our results showed similar performance of both the 3K array and GBS‐based SNPs in terms of training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield. The genomic best linear unbiased prediction (GBLUP), Bayes‐B, and random forest models gave similar predictive ability for dry matter yield (DMY) in different harvest–year combinations and for both marker platforms. The multivariate models involving various combinations of secondary traits (simple breeders' field notes and data) resulted in more than twofold increases in predictive abilities compared to the univariate models. Comparison of the 25% top‐performing observed and predicted genotypes showed a higher overlap percentage (30.10%–66.99%) for multivariate GBLUP models compared to the univariate models (27.18%–51.46%). This further elucidates the great potential of multivariate GS models incorporating the more robust and easily reproducible 3K array SNP markers for improving the genetic gains of DMY in breeding programs.