Nepal's construction industry faces persistent challenges including payment delays, contract management inefficiencies, supply chain opacity, and systemic governance failures. Blockchain technology offers potential solutions through smart contracts, immutable record-keeping, and transparent shared ledgers. This paper systematically reviews blockchain applications in construction management and documented challenges in Nepal's construction sector from peer-reviewed studies. The review identifies four primary blockchain applications: smart contracts for payment automation, supply chain traceability, immutable document management, and shared transparent ledgers. Documented challenges in Nepal include payment delays (RII=0.80-0.92), contract management inefficiencies (78.6% expert agreement), low bidding averaging 37.52% below tender prices, NPR 20 billion in outstanding contractor payments, land acquisition taking 2-3 years, tree cutting approval taking 2 years, average project lag of 37 months with only 15% completed on time, and 17 National Pride Projects requiring 41 years to complete at current funding levels. Blockchain can strongly address payment delays, document coordination, supply chain tracking, and transparency. Blockchain technology offers targeted solutions for specific documented challenges in Nepal's construction sector.

Global textile supply chains include multiple production stages across different regions, which creates challenges for coordination and monitoring. Limited traceability across these stages results in gaps in visibility and makes verification of material origins and production practices difficult. This study examines traceability systems in multi-tier apparel supply chains, with emphasis on digital documentation frameworks that record supplier transactions, material flows, and production activities. A qualitative analytical approach is used to review documentation practices and traceability mechanisms within international textile production networks. The analysis covers supply chain mapping, documentation structures, and system integration across production tiers. Results indicate that structured traceability records improve visibility, support compliance monitoring, and reduce fragmentation of information among supply chain participants. These systems allow organizations to track production processes with greater consistency and detect irregularities in material sourcing and supplier activities. The study also presents a traceability framework that integrates supplier databases, production records, and digital tracking technologies within a unified system. This framework supports consistent documentation and improves coordination across global apparel supply chains, contributing to transparent and accountable production practices.

We investigate the nonequilibrium relaxation dynamics of the one-dimensional (1D) Fermi-Hubbard model subjected to abrupt, global interaction quenches. Specifically, we benchmark the convergence properties, structural accuracy, and algorithmic breakdown of the Magnus expansion against numerically exact results obtained via full Exact Diagonalization (ED) on small, periodic lattice clusters. By tracking the real-time evolution of local observables, double occupancy (doublon density), and many-body state fidelity metrics, we map out the validity bounds of the low-order Magnus series across weak, moderate, and strong interaction regimes. Our findings demonstrate that while the Magnus expansion provides an exceptionally accurate description of short-time coherent dynamics, rapid phase matching, and initial prethermalization tendencies, its convergence is fundamentally bottlenecked at longer timescales. This breakdown is driven by the rapid growth of multi-particle entanglement, non-local operator spreading via nested commutators, and the emergence of severe state-space fragmentation inherent to dense, strongly interacting many-body spectra.

Buildings account for a substantial share of global energy consumption and greenhouse gas emissions, creating an urgent need for design strategies that simultaneously address operational performance, occupant comfort, and life-cycle environmental impacts. While simulation-based optimization has become increasingly common in building performance research, relatively few studies evaluate energy use, thermal comfort, and embodied carbon within a unified tri-objective framework. This study presents a simulation-based tri-objective Pareto optimization of residential buildings in Riyadh, Saudi Arabia, and Dubai, United Arab Emirates, using DesignBuilder, EnergyPlus, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). A standardized four-story residential apartment prototype comprising 16 thermal zones and 2239.82 m² of conditioned floor area was developed and simulated under identical geometric, operational, and HVAC assumptions. Window-to-wall ratio, glazing type, external shading depth, and cooling setpoint temperature were optimized to minimize annual site energy consumption, ASHRAE 55 thermal discomfort hours, and embodied carbon emissions. Baseline simulations revealed substantially higher operational demand in Dubai, with annual energy consumption reaching 272,077 kWh compared with 196,478 kWh in Riyadh, while discomfort hours increased from 2,530 h/year to 3,262 h/year. Optimization reduced annual energy demand by 72.9% in Riyadh and 74.5% in Dubai, while thermal discomfort was reduced to 776 h/year in the best-performing comfort solution. Pareto-optimal solutions consistently favored low window-to-wall ratios (10–16%), high-performance glazing, and external overhangs between 1.5 and 2.0 m. The findings demonstrate the effectiveness of tri-objective optimization for balancing operational efficiency, occupant comfort, and embodied carbon while providing climate-responsive façade design guidance for residential buildings in hot-arid Gulf environments.

The accelerating depletion of fossil fuel reserves, increasing anthropogenic carbon emissions, and growing industrial demand for sustainable chemical manufacturing have intensified global efforts toward the development of highly efficient catalytic systems and renewable energy technologies. Conventional catalytic materials frequently suffer from poor atom utilization efficiency, limited active-site accessibility, catalyst deactivation, and inadequate long-term stability under harsh operational environments, thereby restricting their applicability in environmentally benign synthesis and advanced energy conversion systems. In this context, functional nanomaterials have emerged as transformative catalytic platforms owing to their tunable electronic structures, exceptionally high surface-to-volume ratios, quantum confinement effects, defect-rich architectures, and synergistic interfacial properties. These unique physicochemical characteristics enable superior catalytic activity, enhanced selectivity, accelerated charge-transfer kinetics, and minimized energy consumption in diverse green synthetic processes and sustainable energy applications. Recent advances in nanostructured catalysts, including heteroatom-doped carbon frameworks, metal-organic frameworks, single-atom catalysts, plasmonic nanostructures, layered transition-metal dichalcogenides, perovskite-derived composites, and hybrid semiconductor interfaces, have significantly improved atom economy and reaction efficiency in photocatalytic, electrocatalytic, and thermocatalytic transformations. Furthermore, the integration of multifunctional nanocatalysts into hydrogen evolution systems, oxygen reduction reactions, carbon dioxide reduction technologies, fuel cells, metal-air batteries, supercapacitors, and next-generation solar energy devices has opened new pathways toward carbon-neutral energy infrastructures (Faazal et al., 2023). Emerging fabrication strategies involving defect engineering, surface functionalization, hierarchical nanoarchitectures, and machine-learning-assisted catalyst design are further accelerating the discovery of highly durable and scalable catalytic materials. This review highlights the novelty of integrating multifunctional nanocatalysts with sustainable energy technologies through atom-efficient reaction engineering and environmentally compatible synthesis pathways. Particular emphasis is placed on the structure–property–performance relationships governing catalytic efficiency and energy-device integration. This article aims to critically analyze recent progress, unresolved scientific challenges, and future opportunities associated with functional nanomaterials for sustainable catalytic chemistry and advanced clean-energy systems.

Kebbi state is a region in Northern Nigeria blessed with reasonable resources potentials of both solar and wind energy, but faced with lot of crises of energy supply and distributions due to improper distribution network as results of systems collapse and inadequate utilization of the renewable energy resources such as wind and Solar. This study assesses wind and solar resources in three selected locations of Kebbi state (Argungu, Jega, and Yauri) from the three different senatorial districts across the state using NASA POWER data from 2000 to 2022 and the HOMER optimisation tool, a hybrid renewable system was created for a rural community. The goal was to keep the Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) as low as possible while keeping the capacity deficit below 1%. The solar resource assessment shows a lot of promise. The average global horizontal irradiance ranges from 4.80 to 5.68 kWh/m²/day in Yauri and from 5.30 to 5.88 kWh/m2/day in Argungu and Jega 5.30 to 5.88 kWh/m²/day in Argungu and Jega. The wind resources are not too good, average speeds at 50 m height are between 2.83 and 3.17 m/s for all the selected locations. The results of the optimisation show that a PV-battery-converter hybrid system being the best option for all the selected locations. Wind turbines aren’t the best because they don’t work well in low wind speeds. The study revealed that solar PV-battery systems are a technically feasible and cost-effective way to deploy electricity to rural areas in Kebbi State. This gives policymakers and investors a data-driven way to use decentralized renewable energy as compliment to the national grid.

Downward Longwave Radiation (DLR) plays a crucial role in sustaining the temperature of the Earth’s surface and is vital for maintaining the planet’s energy equilibrium. In this study, eight different emissivity equations were utilized to estimate DLR models and to investigate which is more suitable for evaluating DLR in Ikeja, when statistically tested using five validation indices of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-statistic and Index of Agreement (IA). The impact of some meteorological parameters on DLR was investigated. The data used were obtained from the National Aeronautics and Space Administration (NASA) for a period of 39 years (1984 to 2022), the meteorological parameters are monthly average temperature, relative humidity (RH), DLR and Global Solar Radiation (GSR). Findings indicated that Ikeja recorded its highest value of DLR in April with 425.6915 Wm-2, and its lowest value was in January with 406.2774 Wm-2. The Kruk et al. model was found more accurate for evaluating DLR in Ikeja, indicating that in the absence of measured DLR data, Kruk et al. model is highly recommended for estimating DLR in Ikeja. As the temperature is low during the rainy season, the DLR is high and as the temperature is high during the dry season, the DLR is low. The DLR and RH are high during the rainy season and low during the dry season. The average DLR and GSR values obtained were found to be 418.1707 Wm-2 and 195.5164 Wm-2 respectively, this indicate that the DLR values are twice as much as the GSR during the period under investigation.

This research work focused on the variability of global solar radiation over the area of extension site which is situated in Federal Polytechnic Oko, Orumba North Local Government Area, Anambra State, Nigeria. (6°20'N. 7U00'E) which was located in South Eastern part of Nigeria for the month of December 2016. The global solar radiation was measured every thirty minutes from 6:00am to 6:00pm for the period of five days. To measure the intensity of solar radiation in a particular geographical area is one of the necessary tools used for the investigation of the intensity of solar power radiation and necessary for the implementation of photovoltaic systems in that particular geographical area. To determine the solar radiation intensity, data were collected over a given period of days using an instrument called solarimeter. Solarimeter is an instrument used to determine the intensity or thermal radiation and photovoltaic principles of the sun in a particular geographical area. The data collected were analyzed to observe the behavior or the data and what the data portrays. The data were analyzed using radial plot, line plot, scatter plot main effect, correlations and probability plots. From the analysis, it was observed that the Sun radiation is highest from around 12 noon to 2 pm of the day time and lowest around 6AM to 7AM in the morning hours and around 6 PM in the evenings of 6th to 10th February, 2017. The high intensity is as a result of high atmospheric temperature in the area. The correlations of the intensity and the temperature reveals that they are correlated to each other. The probability plots show that the exponential probability plots are more significance than normal probability plots. The result shows the intensity of the sun light is high in afternoon and lower in the early hours of mornings and late hours of evenings. The average solar intensity of extension site in Federal Polytechnic Oko, is 356,644w/m2. The result will help in positioning solar panels, in order to determine the efficiency of solar panel, being critical in the selection of solar panels that will be necessary and more effective in that particular geographical area.

Traditional wastewater treatment infrastructure, heavily reliant on aerobic processes like activated sludge, operates as a significant net energy sink, consuming approximately 1–3% of global electricity production primarily for mechanical aeration. This operational model overlooks the substantial chemical energy (~16.1 kJ/g COD) embedded within organic pollutants. Microbial Electrochemical Systems (MESs) including Microbial Fuel Cells (MFCs) and Microbial Electrolysis Cells (MECs) represent a foundational shift in environmental biotechnology by leveraging electrochemically active bacteria (EAB) for extracellular electron transfer (EET) directly to solid-state electrodes. This article provides a comprehensive academic review of MES principles, evaluating fundamental thermodynamics, advancements in electrode material engineering, and diverse resource recovery pathways, including direct electricity generation, biohydrogen production, and targeted nutrient mining (e.g., struvite precipitation). Key bottlenecks limiting real-world scalability, such as high internal ohmic resistance, mass-transfer constraints, and capital expenditures, are critically analyzed. We propose a framework for integrating MESs into hybrid treatment trains to achieve process intensification, establishing a pathway for transitioning wastewater management facilities into circular bio-economy "resource mines."

This study presents an explainable artificial intelligence framework for climate-responsive residential envelope design in Gulf coastal cities by integrating building performance simulation, multi-objective optimization, machine learning, and explainability analysis. While previous studies have largely focused on minimizing energy consumption, limited research has simultaneously considered energy performance, capital cost, and thermal comfort within a unified and interpretable decision-support framework. The objective of this research was to identify dominant envelope design variables and derive practical design recommendations for residential buildings located in Dubai, Doha, and Manama. A two-story detached villa prototype was developed and simulated under representative coastal hot-arid climate conditions. Six envelope and operational design variables, including window-to-wall ratio (WWR), shading depth, cooling setpoint, glazing type, wall construction, and roof construction, were evaluated through a simulation-based optimization framework. A total of 600 design alternatives were generated using NSGA-II optimization and subsequently used to train Random Forest predictive models for energy use intensity (EUI), capital cost, and ASHRAE 55 thermal discomfort hours. SHAP (Shapley Additive Explanations) analysis was then applied to quantify variable importance and extract interpretable design rules. The results demonstrated strong predictive capability, with Random Forest models achieving R² values of 0.933 for EUI, 0.982 for capital cost, and 0.955 for thermal discomfort. SHAP analysis revealed that WWR was the dominant driver of energy performance, accounting for 65.2% of total feature importance, while wall construction exerted the greatest influence on capital cost. Thermal comfort was primarily governed by cooling setpoint, followed by WWR and shading depth. Dependence analysis further identified clear threshold relationships between envelope variables and performance outcomes. The proposed framework transforms optimization datasets into actionable design knowledge and provides interpretable decision support for architects, consultants, and developers seeking cost-effective and climate-responsive residential envelope solutions in Gulf coastal environments.