Comparative Study on Insecticidal Activity of Permethrin with Dust Formulated Essential Oils of Monodora myristica, Syzgum caryophyllatum (L) Alston and Pinus slvestris

A study was conducted to determine the insecticidal activity of essential oils of Monodora myristica African Nutmeg, Pinus sylvestris pine essential oil and Syzgum caryophyllatum (l) alston clove essential oil on Acanthoscelides obtectus bean weevil, Camponotus pennsylvanicus Carpenter ant and Sitophilus oryzae rice weevil at different exposure time. The essential oils were obtained from the plant materials by steam distillation using Clavenger type apparatus. The major components of the essential oils were determined using Gas Chromatography-Mass Spectrometry. The essential oils were formulated with clay and chalk which serve as the carrier 5%w/w using acetone as the co-solvent. A control formulation was also prepared by mixing 1.5ml of the acetone with chalk and clay respectively. It was observed that the essential oil of Syzgum caryophyllatum (l) alston has the highest insecticidal activities followed by Monodora myristica and lastly Pinus sylvestris. Permethrin also has high insecticidal activities but depreciate fast on exposure. The major components of essential oil of Pinus sylvestry are α-pinene 27.17%, 3-Cyclo 21.82%, borneol 6.75%, Syzgum caryophyllatum (l) alston constituents are eugenol 75.90%, Eugenol acetate 17.53%, benzene, 1-ethyl-3-nitro 9.12%, benzoic acid, 3-(1-methylethyl) 7.95% and β–caryophyllene 5.91% and Monodora myristica with Linalool 91%, Sabinol-cis 17.87%, tr-13-octadecenoie 25.18% and palmitic acid 7.66%. The essential oils of Pinus sylvestry, Monodora myristica and Syzgum caryophyllatum (l) alston have insecticidal activity

Consensus Algorithms and Deep Reinforcement Learning in Energy Market: A Review

Blockchain (BC) and artificial intelligence (AI) are often utilised separately in energy trading systems (ETS). However, these technologies can complement each other and reinforce their capabilities when integrated. This paper provides a comprehensive review of consensus algorithms (CA) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic related issues. Hence, the motivation to combine BC with AI to realise secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS

Extraction of Dyes from Sunflower Petal and Their Fourier Transform Infrared Characterization

Three solvents of different polarities (water, methanol and 1% NaOHsolution) were used to extract dyes that produced different shades fromdried sunflower (Helianthus annuus) petal. The extraction proceduresusing different solvent types were carried out separately. The dye extractswere thereafter subjected to Fourier Transform Infrared Spectrometry(FT-IR) analysis for characterization in terms of functional groups. Theintensities of the extracted dyes on the shade of colours obtained on piecesof cotton material varied from yellow in methanolic extract to light yellowin aqueous and black in 1% NaOH solution extracts. The results obtainedfrom the FT-IR analysis revealed the presence of several useful functionalgroups such as N-H, C=H, O-H and C=O in the extracts

Stacked recurrent neural network for botnet detection in smart homes

Internet of Things (IoT) devices in Smart Home Network (SHN) are highly vulnerable to complex botnet attacks. In this paper, we investigate the effectiveness of Recurrent Neural Network (RNN) to correctly classify network traffic samples in the minority classes of highly imbalanced network traffic data. Multiple layers of RNN are stacked to learn the hierarchical representations of highly imbalanced network traffic data with different levels of abstraction. We evaluate the performance of Stacked RNN (SRNN) model with Bot-IoT dataset. Results show that SRNN outperformed RNN in all classification scenarios. Specifically, SRNN model learned the discriminating features of highly imbalanced network traffic samples in the training set with better representations than RNN model. Also, SRNN model is more robust and it demonstrated better capability to effectively handle over-fitting problem than RNN model. Furthermore, SRNN model achieved better generalization ability in detecting network traffic samples of the minority classes

Hybrid Deep Learning for Botnet Attack Detection in the Internet of Things Networks

Deep Learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained IoT devices. In this paper, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of Long Short-Term Memory Autoencoder (LAE). In order to classify network traffic samples correctly, we analyse the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep Bidirectional Long Short-Term Memory (BLSTM). Extensive experiments are performed with the BoT-IoT dataset to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92-27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model under-fitting and over-fitting. It also achieves good generalisation ability in binary and multi-class classification scenarios

Federated Deep Learning for collaborative intrusion detection in heterogeneous networks

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks

Data-driven optimal planning for hybrid renewable energy system management in smart campus: a case study

Academic and research institutions need to be at the forefront of research and development efforts on sustainable energy transition towards achieving the 2030 Sustainable Development Goal 7. Thus, the most economically feasible hybrid renewable energy system (HRES) option for meeting the energy demands of Covenant University was investigated in this study. Several optimal combinations of energy resource components and storage which have significant potentials within the university campus were modeled on HOMER software in grid-connected mode. The daily energy consumption data of Covenant University were measured using EDMI Mk10E digital energy meter for a whole year. Data for analyzing renewable energy potentials for several years were sourced from the NASA database through the HOMER platform. Significantly, due to the fluctuating price of diesel fuel in Nigeria, sensitivity analysis was carried out for each combination using diesel fuel prices ranging from 0.3 $/litre to 1$/litre. The results of each projected combination which gave 32 simulation scenarios, were analyzed comparatively using eight important system performance indices which cover economic, technical, and environmental impact assessment with and without battery energy systems. The results of the comparative analysis showed that the PV-Diesel-Grid-BESS HRES is the best configuration for meeting the Covenant university load demands in terms of credible reduction in the net present cost and cost of electricity. However, deployment of the wind energy system is economically infeasible at the study site, while the diesel generator should be strictly a backup

Federated deep learning for intrusion detection in Consumer-Centric Internet of Things

Consumer-centric Internet of Things (CIoT) will play a pivotal role in the fifth industrial revolution (Industry 5.0) but it exhibits vulnerabilities that can render it susceptible to various cyberattacks. Recent studies have explored the potential of Federated Learning (FL) for privacy-preserving intrusion detection in IoT. However, the development of the FL models relied on unrealistic and irrelevant network traffic data, while also exhibiting limitations in terms of covered attack types and classification scenarios. In this paper, we develop Federated Deep Learning (FDL) models using three recent and highly relevant datasets, covering a wide range of attack types as well as binary and multi-class classification scenarios. Our findings demonstrate that the FDL models not only achieve high classification performance, comparable to traditional Centralized Deep Learning (CDL) models, in terms of accuracy (99.60±0.46%), precision (92.50±8.40%), recall (95.42±6.24%), and F1 score (93.51±7.76%) but also exhibit superior computational efficiency compared to their CDL counterparts. The FDL approach reduces the training time by 30.52-75.87%. These classification performance and computational efficiency were achieved through multiple rounds of distributed local training in FDL. Therefore, the proposed FDL framework presents a robust security solution for designing and deploying a resilient CIoT

CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers’ smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions

Self Ear Cleaning: Prevalence and Profile among School Children in Ekiti, Nigeria

Self-cleaning of ears with different objects is a common practice among school children with scanty report in literature. Aim: The aim of this study was to determine the prevalence and profile of self-ear cleaning among school children. Methods: It was a cross-sectional institutional-based study which was carried out among school children in Ekiti, south western Nigeria from January 2017, to March 2017. Results: A total of 174 students participated in this study. Their age ranged between 13 and 17 years. The highest number of participant was found at the age of 15 years. One hundred and eighteen (67.8%) of them had carried out self ear cleaning. Personal hygiene was the commonest reason for self ear cleaning in 28.8% of the students. Cotton buds were mostly used by the respondents in 51.7% of them. Injury to the external auditory canal (EAC) was the most recorded complications. Conclusion: The outcome of this study shows that self-ear cleaning practices were common among school children with a prevalence of 67.8%. Cotton buds were the commonest objects used. Avoidable complications were reported among respondents. There is a need to intensify efforts on public enlightenment programme and the establishment of school health programme in our various schools