Enhanced Firefly Algorithm Inspired by Cell Communication Mechanism and Genetic Algorithm for Short-Term Electricity Load Forecasting

Electricity load forecasting plays a pivotal role in energy management systems, enabling efficient resource allocation and optimal power grid operation. This paper proposes a hybrid approach for short-term electricity load forecasting by integrating a neural network model with the enhanced firefly algorithm (EFA), inspired by cell communication mechanisms, and a genetic algorithm (GA). The proposed methodology leverages the neural network's ability to capture complex patterns from historical load data while utilizing metaheuristic optimization techniques to enhance forecasting accuracy. The EFA, designed to improve exploration and exploitation capabilities, refines parameter selection within the optimization process, while the GA further fine-tunes neural network parameters to enhance model performance. Extensive experimentation on Nigeria’s TCN-NCC electricity load dataset demonstrates the effectiveness of this approach. The hybrid CCMFA-GA-ANN model achieves a mean absolute percentage error (MAPE) of 1.07%, outperforming other benchmark models such as CCMFA (1.26%), BA (1.22%), FA (1.21%), and GA (1.19%). The model also achieves the lowest mean absolute error (MAE) of 48.00 and the highest forecast efficiency of 0.52. Additionally, the Pearson correlation coefficient of 0.99969 and a coefficient of determination (R²) of 0.99999 indicate a strong agreement between actual and predicted values. With a rapid convergence time of 2.321 seconds, the hybrid approach ensures computational efficiency, making it suitable for real-time forecasting applications.These results highlight the significant improvements in forecasting accuracy achieved by the proposed approach compared to conventional methods. The model’s high accuracy and efficiency make it a valuable tool for energy management systems, aiding decision-making in grid operations, demand-side management, and infrastructure planning.    

Effect of S-Glassfibre Loading on the Morphology and Hardness Properties of Epoxy Composites

 In this research work, the effect of S-glass fibre loading on the hardness properties of epoxy material is studied. The s-glass fibre addition enhanced the hardness property of epoxy composites with preeminent formulations found to be 60:40 wt % epoxy to s-glass fibre composition with hardness value of 93.0 HRA and improvement of 16% when compared to the control sample A. Various percentages of S-glass fibre were use to fabricate S-glass fiber/epoxy composites using hand layout method with open mould. The hardness properties of the composites were characterized using Rockwell harness tester. Scanning electron microscopy (SEM) was used to study the distributions of S-glass fibre inside the composites. It was establish that S-glass fibre addition have a good improvement on the hardness property on epoxy material. Furthermore, the more the addition of S-glass fibre into the epoxy, the better the hardness values of the composites until saturation points and after the saturation, the hardness values begins to diminish due to poor interface between the matrix and the reinforcements (s-glass fibre). Please see SEM images for details

Automatic indoor Temperature Controlled Electric Fan

In this study, we report the development of an automatic indoor temperature-controlled electric fan, enabling the automation of the regulation of the ambient temperature. The manual physical control of the indoor electric ceiling fans can be challenging and inconvenient, especially in facilities for the infants, the disabled, or handicapped people. It is also crucial for the indoor fans deployed in farm houses for livestock to be automated, such that the fan speed can be automatically regulated by the ambient temperature. That way, the operation of the indoor electric fans dependent on manual control will be eliminated making the device more effective and efficient without relying on human cognitive ability.   The developed prototype device utilizes an LM35 temperature sensor, an LM339 comparator, and a relay switch to actuate and automatically control the speed of the rotation of the ceiling fan, generating a cooling breeze that enhances the transfer of heat via convection. In this setup, the indoor electric fan rotation speed changes automatically through a five-speed range in ascending order levels. Another unique improvement of this device is in the design of the shape of the fan blades, width, length, and total number of three in a star arrangement that enhanced the velocity of the air distribution within its circulation field. The trial tests using the developed prototype automatic indoor temperature-controlled electric fan showed improved room ventilation up to 20% and a more efficient fan aerodynamics rotating with reduced noise and with a uniform air distribution pattern in the room

Specification Procedure For Symmetric Smooth Transition Autoregressive Models

Abstract: In this paper, we assess the first-rate specification accuracy of Escribano-Jorda procedure (EJP) over Terasvirta procedure (TP) in the selection of true symmetric STAR model of the financial time series. Daily nonstationary BETAGLASS stock index (BSI) totaling 2472 observations were obtained from Nigerian Exchange Limited for empirical illustrations. Terasvirta sequential tests and Escribano-Jorda tests were carried out; first-order logistic function classified as asymmetric transition function and exponential function classified as symmetric transition function were specified by TP and EJP, respectively.  Both symmetric and asymmetric STAR models were justifiably fitted to percentage BETAGLASS stock returns (PBSR) and the best model was determined at the evaluation stage. The empirical assessment of the fits of both symmetric STAR models and asymmetric STAR models revealed that symmetric STAR models outperformed asymmetric STAR models under consideration. Hence, EJP has greater specification power over TP particularly when the true model of the financial time series is any symmetric STAR model. Owing to the presence of autoregressive conditional heteroscedastic (ARCH) effects, STAR-generalized ARCH (STAR-GARCH) models and autoregressive-GARCH (AR-GARCH) models were specified and fitted to PBSR. On balance, the SPLSTAR-GARCH (1, 1) model with generalized hyperbolic skew-student’s t innovations outperformed the competing models. Also, the overall prediction performance of the SPLSTAR-GARCH (1 1) model is better than its linear counterpart based on the Akaike information criterion and forecast root mean square error

Optimized Fast R-CNN for Automated Parking Space Detection: Evaluating Efficiency with MiniFasterRCNN

Automated parking space detection is a crucial application of computer vision in intelligent transportation systems. In this study, we developed a Fast R-CNN-based model for classifying and localizing parking spaces into empty and occupied categories. The model architecture consists of a pre-trained CNN backbone (ResNet50) for feature extraction, a Region Proposal Network (RPN) for generating potential bounding boxes, and Region-of-Interest (RoI) pooling for feature refinement. The classification head utilizes a softmax activation function with cross-entropy loss, while the bounding box coordinates are refined using smooth L1 loss. To facilitate training, we employed Roboflow for dataset annotation, creating ground truth bounding boxes for parking spaces. The model was fine-tuned using transfer learning, leveraging knowledge from the COCO dataset. Training involved hyperparameter optimization, including learning rate scheduling and weight decay, to enhance convergence. Model selection was based on validation loss and accuracy to ensure generalization to unseen data. The model was deployed using Gradio, allowing real-time parking space detection from uploaded images. Despite achieving a final loss of 0.8280, the model exhibited some background noise distortions, impacting detection accuracy. To address this limitation, we explored a lightweight alternative, MiniFasterRCNN, optimized for efficiency with a simpler architecture. The MiniFasterRCNN was trained on a three-class dataset (empty, occupied, background), achieving a validation accuracy of 77.78%. However, attempts to achieve 100% accuracy proved inefficient, highlighting the need for further improvements, such as segmentation techniques (Masked R-CNN). This research demonstrates the feasibility of Fast R-CNN-based models for parking space detection while emphasizing the importance of architectural optimizations and hyperparameter tuning for improved accuracy and robustness in real-world application

Phytochemical, Anti-inflammatory, Antioxidant, Toxicity and Antimicrobial Activities of Sarcophrynium brachystachys (Benth) K. Shum Leaves

This study investigates the phytochemical composition and pharmacological properties of Sarcophrynium brachystachys leaf extract. The extract was obtained through ethanol percolation and subjected to qualitative and quantitative phytochemical analyses, revealing significant amounts of flavonoids (5.51 mg/100 g), alkaloids (6.68 mg/100 g), and saponins (4.28 mg/100 g). Antibacterial screening against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa demonstrated notable inhibitory effects, particularly with the methanol fraction showing zones of inhibition ranging from 12±1.03 mm to 15±1.23 mm. Acute toxicity evaluation revealed no mortalities across all dose groups, indicating a favorable safety profile. Anti-inflammatory assessment exhibited dose-dependent reductions in paw circumference post-induction, with percentage inhibition ranging from 34.07% to 40.98% compared to aspirin (56.92%). Furthermore, in vitro antioxidant assays demonstrated dose-dependent scavenging activity against DPPH radicals (18.90% to 73.74%), nitric oxide radicals (13.10% to 73.16%), and ferric ions (3.91% to 53.92%). These findings underscore the therapeutic potential of S. brachystachys leaf extract as a source of natural bioactive compounds with antibacterial, anti-inflammatory, and antioxidant properties, warranting further investigation for pharmaceutical applications

Seasonal Short-Term Load Forecasting (STLF) using combined Social Spider Optimisation (SSO) and African Vulture Optimisation Algorithm (AVOA) in Artificial Neural Networks (ANN)

Accurate short-term load forecasting (STLF) is critical for efficient energy management, especially in regions like Nigeria, where electricity demand fluctuates due to climatic and socio-economic factors. This study proposes a hybrid model combining Social Spider Optimisation (SSO) and African Vulture Optimisation Algorithm (AVOA) to optimise Artificial Neural Networks (ANN) for improved STLF accuracy. The model was trained and validated using actual load data from the Nigerian grid for February, March, May, and June 2021. Quantitative evaluation using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient, and Coefficient of Determination (R²) showed superior performance of the SSO-AVOA model. The most stable results were recorded in May 2021, with MAPE of 0.202%, MAE of 8.47 MW, RMSE of 28.83 MW, and R² of 0.999, indicating nearly perfect forecasting. February and June periods showed relatively higher errors (e.g., MAPE up to 1.043% in February), reflecting the difficulty of forecasting during seasonal transitions. Findings confirm the robustness and adaptability of the hybrid model, which consistently maintains high correlation between actual and forecasted loads. However, error patterns during volatile periods suggest potential for improvement. Future work should integrate weather and socio-economic indicators, apply dynamic seasonal adaptations, and validate the model across Nigeria’s geopolitical zones. This study demonstrates that hybrid bio-inspired algorithms like SSO-AVOA are practical, high-performing tools for real-world load forecasting in dynamic and complex environmen

On the Study of Kumaraswamy Reduced Kies Distribution: Properties and Applications

Unit-bounded distributions play a crucial role in probability and statistics for modeling quantities that are strictly confined between 0 and 1, such as rates, ratios, proportions, and percentages. Despite their importance, these distributions are relatively scarce compared to those with unbounded support, even though many real-world phenomena involve data restricted to a unit interval, including proportions, percentages, ratios, rates, and fractions. Some unit distributions arise naturally from analytical derivations, while others emerge through generalization from distributions originally defined over broader domains. This study introduces a three-parameter unit-bounded distribution, termed the Kumaraswamy Reduced Kies Distribution, developed through a generalization process of Kumaraswamy G-family of distribution based on the function of functions approach applied to the Reduced Kies Distribution proposed. The Kumaraswamy Reduced Kies Distribution, a flexible three-parameter distribution with semi-bounded support, serves as the foundation for extending this adaptability to the unit interval. The probability density function of the proposed distribution exhibits a variety of shapes, including J, reversed-J, left-skewed, symmetric, and bathtub unimodal forms. Additionally, its hazard rate function follows a monotonically non-decreasing pattern. Several statistical properties and reliability measures are examined, including the survival function, hazard rate function, cumulative hazard function, reversed hazard function, odd function, quantile function, median, skewness, kurtosis, and order statistics. The estimation of model parameters is performed using Maximum Likelihood Estimation, Maximum Product of Spacing, and Cramer-von Mises methods. Monte Carlo simulations are conducted to assess the effectiveness of these estimation techniques, demonstrating that Biases, Mean Squared Errors, and Mean Relative Errors decrease as the sample size increases. Finally, the practical applicability of the proposed model is illustrated using two real-life datasets. A comparative analysis confirms that the proposed model achieves a superior fit compared to several existing models.

Projecting AI-Driven Intersection of FinTech, Financial Compliance, and Technology Law

This review paper provides a comprehensive exploration of the evolving intersection between FinTech innovations, financial compliance, and technology law. It critically analyzes the transformative impact of disruptive technologies, including blockchain, artificial intelligence (AI)-driven financial services, digital payment systems, and robo-advisors, on the financial sector. The study delves into key compliance challenges such as data privacy and protection, anti-money laundering (AML) requirements, Know Your Customer (KYC) protocols, and the increasing need for advanced cybersecurity measures. Additionally, it underscores the critical role of technology law in fostering innovation through frameworks such as regulatory sandboxes and cross-border regulatory harmonization, while promoting ethical and legal financial practices. By identifying emerging trends such as AI-powered compliance solutions, enhanced data security protocols, and ethical AI deployment, this paper offers strategic recommendations to navigate the complexities of regulatory compliance without stifling innovation. The findings provide actionable insights for policymakers, industry stakeholders, and researchers aiming to foster a secure, efficient, and innovative financial ecosystem

Review of the Environmental Impact of Polymer Degradation

Polymer degradation has emerged as a significant environmental concern due to the persistence of plastic waste in ecosystems and the release of harmful byproducts. As polymers degrade, they break down into microplastics and toxic chemicals, which contribute to soil, water, and air pollution, posing serious risks to ecosystems and human health. The degradation of polymers, such as polyethylene, polyvinyl chloride (PVC), and polypropylene, releases hazardous substances like phthalates, dioxins, and heavy metals, which contaminate the environment and disrupt food chains. Microplastics, in particular, have been shown to infiltrate aquatic and terrestrial ecosystems, leading to bioaccumulation in wildlife and potential harm to human health. Additionally, polymer degradation can contribute to climate change through the release of greenhouse gases, especially when polymers are disposed of in landfills or incinerated. The environmental impact of polymer degradation is especially profound in marine environments, where plastics threaten biodiversity and ecosystem services. This review examines the various mechanisms of polymer degradation, the resulting environmental pollutants, and their implications for human health and ecosystems. It also highlights current challenges in managing polymer waste and proposes strategies, including improved recycling technologies, the development of biodegradable polymers, and enhanced public awareness, to mitigate the adverse effects of polymer degradation. Effective waste management and stricter regulations are essential for addressing this growing issue and promoting a more sustainable future