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Due to rapid urbanization and the recent growing influx of migrants from politically fragile states, cities are now faced with an impending housing problem. In developing countries like Nigeria, the impacts of the housing problem, which include economic, social (humanitarian), and environmental crises, are evident. Recently, sustainability has become a central theme for public discussion, not only in expanding housing for the growing population but also in improving the quality of residential livelihoods of low-income households without compromising the housing needs of future generations. So far, there have been numerous studies conducted on sustainable housing in Nigeria, touching on sustainability in housing but mostly from the professionals’ point of view. Therefore, a document content analysis to generate success criteria and a survey for household validation were conducted. Results show that security ranks the highest, and other criteria of importance include accessibility, adaptability, utility, technology, community, affordability, and acceptability. Hence, the study concludes that social and environmental sustainability in housing should enhance household satisfaction by ensuring the security and welfare of its residents, adapt to its immediate environment, be acceptable, be supported with social amenity to integrate the community through participation, and, finally, manage household utility efficiently.

The adaptability of cassava to low fertile and marginal soils facilitates its production in subsistent agriculture. As a result, smallholder farmers rarely apply fertilizers. The current yield gap is therefore very large, calling for application of fertilizers and soil amendments to improve its productivity. Field experiments were carried out to assess the potential of partially substituting Phosphorus (P) fertilizers by in vitro-produced arbuscular mycorrhizal fungal (AMF) inoculants in cassava production in two agro-ecologies of Nigeria: Northern Guinea Savanna (Samaru) and Sudan Savanna (Minjibir). The experiments were laid out in a split plot design with P levels (0, 17.5, 35 and 52.5 kg P2O5 ha-1) as main plot and AMF inoculants (Control, Glomygel, Glomygel carrier, Mycodrip, Mycodrip carrier) as subplots. The results in Samaru showed that there was significant interaction between AMF and P in root fresh weight, total biomass and root to shoot ratio. The root fresh weights of the inoculated cassava increased proportionally with application of P. However, highest root fresh weight of cassava inoculated with Glomygel was observed at 35 kg P2O5 ha-1 recording 25% yield increase compared to 52.5 kg P2O5 ha-1 application. Interestingly, Cassava inoculated with Glomygel at 17.5 kg P2O5 ha-1 gave root fresh yield statistically similar to where 35 kg P2O5 ha-1 was applied. This represented a 50% reduction in P fertilizer use. Also, cassava inoculated with Glomygel increased leaf nutrient concentrations, which strongly correlated with the root fresh yield. However, no effects of inoculant carriers were observed in yield and nutrient concentrations. Contrarily, there was no significant treatment effect in Minjibir for nearly all the measured parameters. Cassava yield was however, higher in Minjibir than Samaru probably due to soil fertility and structural differences, which resulted in few observable effects of AMF and P treatments at Minjibir. We conclude that under low P conditions inoculation with in vitro produced AMF inoculants could be employed to reduce P fertilizer requirements for cassava and improve yields, but the variability of the responses as a result of soil heterogeneity and the identity of the fungal strain in the inoculant require further investigations before recommending the practice.

ObjectiveEpilepsy prevalence varies widely across Nigeria, with rates ranging from 3.1 to 37.0/1000 population. There have been no studies on epilepsy prevalence and treatment gap in the Northeast Region of Nigeria. This study aimed to study epilepsy prevalence and the epilepsy treatment gap (ETG) in an urban and a rural community in Northeast Nigeria.DesignCross-sectional, community-based survey.SettingEpilepsy screening of residents in two communities in Northeast Nigeria using a WHO screening tool and a validated study questionnaire from 1 March to 10 June 2022.Participants8599 community residents aged ≥2 years.Outcome measuresPrevalence of epilepsy, active epilepsy, ETG and associated factors.ResultsWe screened 8599 residents, of whom 88 had epilepsy. Crude epilepsy prevalence was 10.2 per 1000 and was three times higher in the rural than in the urban community (18.5 vs 6.4; χ2=26.79, p<0.001). The age-adjusted prevalence was 11.3/1,000, whereas the prevalence of active epilepsy was 8.6/1000. ETG was noted in 78.4% participants with active epilepsy and was similar in urban and rural communities (76.7% vs 79.5%; χ2=0.087, p=0.768). Logistic regression analysis showed that the ETG was associated with a lack of counselling (OR 15.8, 95% CI 3.5 to 70.7, p<0.001) but not a prior diagnosis of epilepsy, sex, age, marital status, urban versus rural residence or distance to the health centre ≥2 versus <2 km. Among 20 participants with epilepsy, electroencephalography suggested focal epilepsy in four participants, generalised epilepsy in eight and normal findings in eight, while brain CT scans in six participants were all normal.ConclusionThe prevalence of epilepsy in Bauchi State was within the range reported in Nigeria but three times higher in the rural than in the urban community. A high ETG was associated with poor counselling of people with epilepsy. Epilepsy counselling, health education and wider access to neurology services could reduce the burden of epilepsy in Northeast Nigeria.

Partial shading poses a significant challenge to photovoltaic (PV) systems by degrading power output and overall efficiency, especially under non-uniform irradiance conditions. This paper proposes an advanced time-sharing maximum power point tracking (MPPT) control strategy implemented through a non-isolated single-phase multi-input microinverter architecture. The system enables individual power regulation for multiple PV modules while preserving their voltage–current (V–I) characteristics and eliminating the need for additional active switches. Building on the concept of distributed MPPT (DMPPT), a flexible full power processing (FPP) framework is introduced, wherein a single MPPT controller sequentially optimizes each module’s output. By leveraging the slow-varying nature of PV characteristics, the proposed algorithm updates control parameters every half-cycle of the AC output, significantly enhancing controller utilization and reducing system complexity and cost. The control strategy is validated through detailed simulations and experimental testing under dynamic partial shading scenarios. Results confirm that the proposed system maximizes power extraction, maintains voltage stability, and offers improved thermal performance, particularly through the integration of GaN power devices. Overall, the method presents a robust, cost-effective, and scalable solution for next-generation PV systems operating in variable environmental conditions.

The use of toxic and less bioavailable inorganic selenium can now be supplemented with an alternative organic source from bacterial species in nutrition for human and animal benefit. This study investigated the effects of selenium sources on laying performance, egg quality characteristics, intestinal morphology, caecum microbial population, and digesta volatile fatty acids in laying hens. One hundred and forty-four Lohman Brown Classic laying hens, at 23 weeks of age, were divided into four experimental groups (36 hens in each), differing in form of Se supplementation: no Se supplementation (Con), 0.3 mg/kg of inorganic Se in the form of sodium selenite (Na2SeO3), 0.3 mg/kg of organic Se from selenium yeast (Se-Yeast), and 0.3 mg/kg of organic Se from Stenotrophomonas maltophilia (bacterial organic Se, ADS18). The results showed that different dietary Se sources significantly affected laying rate, average egg weight, daily egg mass, feed conversion ratio (FCR), and live bodyweight (LBW) (p < 0.05). However, average daily feed intake and shell-less and broken eggs were unaffected (p > 0.05) among the treatment groups. The findings revealed that selenium sources had no (p > 0.05) effect on egg quality (external and internal) parameters. However, eggshell breaking strength and Haugh unit were significantly (p < 0.05) improved with organic (ADS18 or Se-yeast) Se-fed hens compared to the control group. In addition, egg yolk and breast tissue Se concentrations were higher (p < 0.05) in the dietary Se supplemented group compared to the control. Intestinal histomorphology revealed that hens fed ADS18 or Se-Yeast groups had significantly (p < 0.05) higher villi height in the duodenum and jejunum compared to those fed Na2SeO3 or a basal diet. However, when compared to organic Se fed (ADS18 or Se-Yeast) hens, the ileum villus height was higher (p < 0.05) in the basal diet group; with the lowest in the SS among the treatment groups. A significant increase (p < 0.05) of Lactobacilli spp. and Bifidobacteria spp., and a decrease of Escherichia coli and Salmonella spp. population were observed in the organic (ADS18 or Se-yeast) compared to inorganic supplemented and control hens. The individual digesta volatile fatty acid (VFA) was significantly different, but with no total VFA differences. Thus, bacterial selenoprotein or Se-yeast improved the performance index, egg quality characteristics, egg yolk and tissue Se contents, and intestinal villus height in laying hens. Moreover, caecum beneficial microbes increased with a decrease in the harmful microbe population and affected individual cecal volatile fatty acids without affecting the total VFA of the laying hens digesta.

In medical diagnostics, brain tumor classification remains essential, as accurate and efficient models aid medical professionals in early detection and treatment planning. Deep learning methodologies for brain tumor classification have gained popularity due to their potential to deliver prompt and precise diagnostic results. This article proposes a novel classification technique that integrates the Xception model with a hybrid attention mechanism and progressive image resizing to enhance performance. The methodology is built on a combination of preprocessing techniques, transfer learning architecture reconstruction, and dynamic fine-tuning strategies. To optimize key hyper-parameters, this study employed the Dynamic Chaotic Whale Optimization Algorithm. Additionally, we developed a novel learning rate scheduler that dynamically adjusts the learning rate based on image size at each training phase, improving training efficiency and model adaptability. Batch sizes and layer freezing methods were also adjusted according to image size. We constructed an ensemble approach by preserving models trained on different image sizes and merging their results using weighted averaging, bagging, boosting, stacking, blending, and voting techniques. Our proposed method was evaluated on benchmark datasets achieving remarkable accuracies of 99.67%, 99.09%, and 99.67% compared to the classical algorithms.

This article presents a review to provide up-to-date research findings on concentrated photovoltaic (CPV) cooling, explore the key challenges and opportunities, and discuss the limitations. In addition, it provides a vision of a possible future trend and a glimpse of a promising novel approach to CPV cooling based on pulsating flow, in contrast to existing cooling methods. Non-concentrated photovoltaics (PV) have modest efficiency of up to around 20% because they utilise only a narrow spectrum of solar irradiation for electricity conversion. Therefore, recent advances employed multi-junction PV or CPV to widen the irradiation spectrum for conversion. CPV systems concentrate solar irradiation on the cell’s surface, producing high solar flux and temperature. The efficient cooling of CPV cells is critical to avoid thermal degradation and ensure optimal performance. Studies have shown that pulsating flow can enhance heat transfer in various engineering applications. The advantage of pulsating flow over steady flow is that it can create additional turbulence and mixing in the fluid, resulting in a higher heat transfer coefficient. Simulation results with experimental validation demonstrate the enhancement of this new cooling approach for future CPV systems. The use of pulsating flow in CPV cooling has shown promising results in improving heat transfer and reducing temperature gradients.

The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar.

Wireless energy harvesting enables the conversion of ambient energy into electrical power for small wireless electronic devices. This technology offers numerous advantages, including availability, ease of implementation, wireless functionality, and cost-effectiveness. Radio frequency energy harvesting (RFEH) is a specific type of wireless energy harvesting that enables wireless power transfer by utilizing RF signals. RFEH holds immense potential for extending the lifespan of wireless sensors and wearable electronics that require low-power operation. However, despite significant advancements in RFEH technology for self-sustainable wearable devices, numerous challenges persist. This literature review focuses on three key areas: materials, antenna design, and power management, to delve into the research challenges of RFEH comprehensively. By providing an up-to-date review of research findings on RFEH, this review aims to shed light on the critical challenges, potential opportunities, and existing limitations. Moreover, it emphasizes the importance of further research and development in RFEH to advance its state-of-the-art and offer a vision for future trends in this technology.

As the metaverse progresses, it requires real-time, low-latency, and secure computing capabilities that conventional cloud-based systems cannot fully support. Multiaccess edge computing (MEC) addresses this demand by moving processing closer to the user; however, it also introduces new challenges in task placement, resource management, security, and trust. The blockchain is a promising enabler to address the limitations of trust, transparency, and centralized control in these systems. This survey systematically reviews 61 high-quality studies that explore blockchain-based solutions for task offloading and resource allocation in MEC. This work identifies the prevailing trends and research gaps using a structured method. The analysis reveals that over 60% of studies employ artificial intelligence-based techniques. Additionally, this work quantifies the adoption of various performance metrics from the literature and examines the case study distribution across application domains. Finally, this work outlines the technical challenges and opportunities for future research toward scalable, efficient, and trustworthy blockchain-enabled MEC frameworks.