Accurate estimation of Downward Longwave Radiation (DLR) is essential for climate modeling, hydrology, agriculture, and renewable energy applications, yet the reliability of widely used empirical DLR models across different climatic zones remains uncertain. This study evaluated eleven existing clear-sky DLR empirical models and developed five new locally calibrated regression models for Ibadan and Makurdi, within the Guinea Savannah climatic zone of Nigeria. Forty years (1984-2023) of monthly mean air temperature, relative humidity, and DLR data were used, with thirty-two years for model development and eight years for validation. Model performance was assessed using Mean Bias Error, Root Mean Square Error, Mean Percentage Error, t-statistic, Index of Agreement, and correlation coefficient, followed by a composite ranking procedure. Results showed that, among existing models, Kruk et al., performed best at Ibadan while Idso was most suitable for Makurdi. Among the newly developed models, the modified Guest model gave the highest accuracy at Ibadan, while the modified Dilley and O'Brien model performed best at Makurdi, with all modified models outperforming their existing counterparts. Seasonal analysis revealed that DLR was consistently higher during the rainy season, driven predominantly by atmospheric water vapour and cloud cover rather than temperature. These findings demonstrate that site-specific calibration substantially improves DLR estimation accuracy and provide validated, region-appropriate models recommended for practical application across the Guinea Savannah climatic zone of Nigeria in the absence of direct pyrgeometer measurements.

The challenges associated with the evolving energy systems call for an intelligent DC energy management system (EMS) crucial for enhanced efficiency, improved reliability, and cooperative energy sharing. This paper presents a strategy of EMS for a DC microgrid with energy-sharing capability through a cooperative battery bank model by employing a SIMULINK/MATLAB simulation. Each nanogrid of the microgrid cluster utilizes an energy flow switching circuit to connect its PV array, DC load, and individual battery unit. The proposed microgrid comprises four nanogrids having individual battery units linked together via switches and a communication link, forming a cooperative bank model. By taking into account the instantaneous PV-load imbalance, the EMS regulates the energy flow at each nanogrid, while allows for energy sharing between nanogrids determined by the state-of-charge (SOC) of the battery bank. The simulated results validate the exactness of the presented method. Furthermore, the approach is simple in terms of both the system’s framework and its management, and also extendable to any number of nanogirds’ cluster using an array of switches for the interconnection among them and their batteries.

An already overburdened national power distribution infrastructure faces serious operational issues as a result of Pakistan's quickly expanding electric vehicle (EV) sector. In addition to limiting the efficient use of available renewable energy, uncoordinated commercial EV charging results in severe peak demand spikes, voltage violations, and increased energy expenditures. A decentralized, machine learning-driven load scheduling architecture for Pakistani commercial EV charging stations is proposed in this study. Using a dataset of 35,040 hourly observations from NEPRA, NTDC, PESCO, and Pakistan Meteorological Department sources, a Random Forest Regression (RFR) model is created using the Python scikit-learn library. It incorporates 14 engineered features, such as time-of-day, feeder loading, ambient temperature, solar irradiance, time-of-use (ToU) tariff windows, and lag-based load features. The trained model attains an R2 of 0.946 and a test Mean Absolute Error (MAE) of 4.73 kW. A priority-weighted scheduling algorithm that maximizes charger activation within transformer capacity and NEPRA voltage regulation restrictions is fed the projected demand profile. In comparison to an unplanned baseline, simulation results for a sample 630 kVA urban Peshawar station show a 27.4% decrease in daily peak demand, a 23.1% decrease in daily energy expenditure, and a 19.8 percentage-point increase in on-site solar PV use. The edge-deployable architecture is ideal for Pakistan's limited SCADA and ICT environment because it does not require real-time cloud access.

The Magnus expansion is attractive for periodically driven quantum systems because every finite truncation exponentiates an anti-Hermitian generator and therefore preserves unitarity. Its practical failure in interacting fermionic systems is nevertheless subtle: a low-order approximation may remain unitary while becoming progressively less accurate as additional terms are retained. We establish order-resolved breakdown signatures for a half-filled, periodically driven one-dimensional Fermi-Hubbard chain. The one-period propagator is computed numerically exactly in the fixed-particle sector and compared with Magnus approximants through fourth order. Failure is defined operationally by a successive-order error ratio (rₙ=εₙ/εₙ₋₁≥ 1), not by an unsupported claim of divergence of the infinite series. Across a raw (9×29) interaction-frequency grid ((U/J=0…,8), (ω/J=2…,16)), at least one added order loses improvement at 36.40% of the 261 sampled points. The smallest sampled frequency above which all retained orders improve throughout the remaining high-frequency tail rises from (ω/J=2.5) at (U=0) to (11.5) at (U/J=8). At (U/J=4), the highest (r₄=1) crossing occurs at (ω/J=7.719), followed by a monotone sampled tail from (ω/J=8). High-frequency error exponents approach the expected omitted-order scaling. A norm-based convergence certificate is consistently more conservative than the observed finite-order boundary. These results show that Magnus breakdown in the Hubbard model is order dependent, interaction shifted, and detectable before unitarity is lost.

This paper presents the findings of a research on an artificial intelligence-based hybrid Support Vector Machine–Bat Optimization Algorithm (SVM-BOA) model developed for spectrum sensing in the Ultra High Frequency (UHF) television band (470 – 694 MHz). The main objective is to enhance detection accuracy, probability of detection, probability of false alarm, and computational efficiency for efficient spectrum utilization. Experimental spectrum measurements were conducted across Kano, Kaduna, and the Federal Capital Territory (FCT), Abuja, Nigeria, to assess the level of spectrum occupancy and identify available Television White Space (TVWS) channels. Relevant signal features, including received signal power level, noise power level, and frequency, were extracted from the field data collected and used to train and test the proposed model. The model was developed and implemented in MATLAB computing environment. The new model was validated using test data for classification of channel status as either free or occupied. The developed model achieved a detection accuracy of 97.67% at a threshold of −99.5dB, based on metric from confusion matrix, outperforming baseline SVM (78.57%) and other benchmark optimization-based SVM models, including Grid Search SVM (92.86%), Random Search SVM (89.29%), Genetic Algorithm SVM (92.86%), and Particle Swarm Optimization SVM (92.78%). It also attained a probability of detection (Pd) of 1.00 (100%) and a probability of false alarm (Pfa) of 0.1053 (10.53%), which satisfies the sensing performance requirements of the Institute of Electrical and Electronics (IEEE 802.22) standards under low-signal conditions. These findings suggest that the hybrid SVM-BOA approach will offer superior classification accuracy and computational efficiency relative to conventional techniques and comparable hybrid models, signifying a strong potential for the improvement of spectrum utilization in next-generation cognitive radio networks.

The search for eco-friendly corrosion inhibitors for mild steel has driven interest in the valorization of expired pharmaceutical products as green alternatives to conventional toxic inhibitors. The present study investigated the synergistic inhibition behaviour of expired ciprofloxacin and iodide ions for mild steel in synthetic acid rain solution using gravimetric, surface morphology analysis and molecular dynamic simulation approaches. The results showed that the corrosion inhibition performance significantly improved in ciprofloxacin-iodide system and the inhibition performance reach approximately 97 % at low concentration. The surface morphology analysis showed that the inhibitor molecules are adsorbed on the mild steel surface, thereby decreasing the corrosion rate. The adsorption process follows the Frumkin adsorption isotherm. The experimental results correlated with the molecular dynamics simulation results. The findings in this research provide a practical strategy for the potential reuse of expired ciprofloxacin waste and the development of novel corrosion inhibitors.

This study proposes an interoperable Management Information System (MIS) that integrates real-time payment intelligence, merchandise planning, and distributed workforce coordination within a unified enterprise information architecture. The proposed framework addresses fragmentation across payment, retail, inventory, and workforce systems through System, Process, and Experience APIs, supported by Apache Kafka and Apache Flink for real-time event processing. Synthetic financial, retail, and workforce datasets are used to evaluate payment-event processing, fraud aware demand signals, inventory decisions, workforce allocation, operational reliability, and governance. A multi-objective planning model considers processing latency, inventory exposure, workforce imbalance, and operational risk. The experimental results indicate that the proposed MIS achieved 86 ms decision latency, compared with 420 ms for event-driven processing and 1,850 ms for batch coordination. Timely payment event processing reached 97%, inventory response reached 91%, and workforce task response reached 88%. The findings indicate that payment activity can serve as a real-time operational signal for merchandise and workforce decisions when supported by governed interoperability, event processing, and audit controls. The study provides an architecture and evaluation framework for integrated decision intelligence across distributed enterprise operations.

The need to improve bioethanol yield from elephant grass has made the optimization of the pretreatment process inevitable. This work studied and determined the optimal alkaline concentration for the pretreatment of elephant grass with sodium hydroxide (NaOH) and hot water under the specified conditions. The pretreatment was performed at different concentrations of sodium hydroxide for a period of 1:30 hr. at 97.5oC. Scanning Electron Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to determine the effects of the pretreatment on the morphology and structure of the elephant grass. The grass hydrolysate was subjected to simultaneous saccharification and fermentation using cellulase enzyme and bakers’ yeast. Optimum pretreatment condition was obtained by using 0.25 M NaOH, which yielded 6.778g/L reducing sugar. Bioethanol yield was found to be 8.84% of the available sugar. The SEM and FTIR analysis showed that NaOH pretreatment under the determined optimum conditions successfully disintegrated the recalcitrant structure of elephant grass. Hence, NaOH pretreatment at the concentration of 0.25 M exposed to hot water for 1:30 hr. at 97.5oC is a promising condition for bioethanol production from elephant grass.

Valorization of silica-bearing plant residues offers a route for converting low-value biomass into functional inorganic materials. This study investigated silica recovery from Bambusa vulgaris leaves using a multistage process comprising hydrochloric-acid leaching, hydrogen-peroxide pretreatment, calcination, alkaline extraction, acid precipitation, washing, and drying. Freshly collected leaves were washed and oven-dried, after which a 50.044 g powder batch was leached with 1 M HCl at 80°C for 1 h. The recovered dry mass after leaching was 31.8 g. A 3.0 g acid-leached sub-batch was then treated with 1 mL of 30% H2O2 in 20 mL distilled water at 60°C for 1 h and dried to 1.942 g before calcination at 650°C for 3 h. A representative gravimetric mass balance showed that approximately 100 g of dry leaf powder produced 20 g of calcined ash and approximately 10 g of dried silica-based powder, corresponding to a 20% ash yield, a 10% recovered-product yield on a dry-leaf basis, and a 50% ash-to-product recovery. The calcined material was subjected to alkaline extraction with 2 M NaOH at 90°C for 1 h, and the resulting sodium-silicate-containing filtrate was acidified with HCl to approximately pH 3. The formed gel was washed and dried at 100°C for 12 h to obtain a white silica-based powder. FTIR analysis over 4000-400 cm-1 showed a broad O-H band centered at 3398 cm-1, a prominent Si-O-Si asymmetric stretching band at 1062 cm-1, a Si-O-related band near 800 cm-1, and a broad Si-O-Si bending region at approximately 470-425 cm-1. These features are consistent with hydrated, silanol-containing silica and with the broad spectral profile commonly reported for biogenic amorphous silica. However, FTIR alone cannot establish crystallographic phase or quantitative purity. The study therefore demonstrates a feasible H2O2-assisted extraction route with competitive gravimetric recovery, while identifying XRD, XRF/EDX, replicated yield measurements, and a matched no-H2O2 control as essential next-stage analyses.

Unknown project risks play a critical role in the successful execution of projects. These risks may originate from internal or external sources and can present opportunities, threats, or neutral outcomes. The objective of this paper is to examine unknown project risks that may affect project outcomes. It discusses the challenges associated with identifying unknown risks and highlights the role of project managers and risk management teams in managing these risks and their potential impacts as well as in transforming unknown risks into known and manageable risks through structured risk identification, investigations and continuous monitoring. Effective identification and management of unknown risks enhance project decision-making, reduce uncertainties, and increase the likelihood of project success. This paper also presents a case study on the implementation of new technologies in Saudi Aramco. The study examines the challenges encountered during implementation and demonstrates how thorough investigations and clarification of identified uncertainties, known and unknown risks contributed to the successful implementation and adoption of these technologies within the company.

Prestressed High-Strength Concrete (PHC) piles have recently been utilized in Saudi Arabia for the Ras Al-Khair Steel Plate Manufacturing Project due to their high structural capacity, durability, and rapid installation. This study evaluates the performance of PHC piles installed using two construction methods: dynamic driving and hydraulic static pressing. The performance of the installation methods was assessed based on the results of axial compression load tests, with a maximum allowable settlement of 25 mm under the highest test load. The static pressing method results on a lower average settlement and narrower range of settlement readings for the tested piles, which suggest that it is more reliable than dynamic driving method.