Smart waterborne disease control for a scalable population using biodynamic model in IoT network.

We propose a biodynamic model for managing waterborne diseases over an Internet of Things (IoT) network, leveraging the scalability of LoRa IoT technology to accommodate a growing human population. The model, based on fractional order derivatives (FOD), enables smart prediction and control of pathogens that cause waterborne diseases using IoT infrastructure. The human-pathogen-based biodynamic FOD model utilises epidemic parameters (SVIRT: susceptibility, vaccination, infection, recovery, and treatment) transmitted over the IoT network to predict pathogenic contamination in water reservoirs and dumpsites in Iji-Nike, Enugu, the study community in Nigeria. These pathogens contribute to person-to-person, water-to-person, and dumpsite-to-person transmission of disease vectors. Five control measures are proposed: potable water supply, treatment, vaccination, adequate sanitation, and health education campaigns. A stable disease-free equilibrium point is found when the effective reproduction number of the pathogens, R0eff<1 and unstable if R0eff>1. While other studies showed a 98.2% reduction in infections when using IoT alone, this paper demonstrates that combining the SVIRT epidemic control parameters (such as potable water supply and health education campaign) with IoT achieves a 99.89% reduction in infected human populations and a 99.56% reduction in pathogen populations in water reservoirs. Furthermore, integrating treatment with sanitation results in a 99.97% reduction in infected populations. Finally, combining these five control strategies nearly eliminates infection and pathogen populations, demonstrating the effectiveness of multifaceted approaches in public health and environmental management. This study provides a blueprint for governments to plan sustainable smart cities for a growing population, ensuring potable water free from pathogenic contamination,in line with the United Nations Sustainable Development Goals #6 (Clean Water and Sanitation) and #11 (Sustainable Cities and Communities).

Utilizing mechatronic agilent gas chromatography to validate therapeutic efficacy of Combretum paniculatum against oxidative stress and inflammation.

The quest for novel antioxidant and anti-inflammatory medications from medicinal plants is crucial since the plants contain bioactive compounds with a better efficacy and safety profile than orthodox therapy. This study harnesses the capabilities of mechatronics-driven Agilent Gas Chromatography, deploying in vitro, in vivo, and in silico models to unravel the antioxidant and anti-inflammatory attributes within Combretum paniculatum ethanol extract (CPEE). Employing gas chromatography-mass spectroscopy (GC-MS), our analysis efficiently segregates and evaluates volatile compound mixtures, a technique renowned for identifying organic compounds, as exemplified by its success in detecting fatty acids in food and resin acids in water. Using gas chromatography-mass spectrometry (GC-MS) and GC-FID analyses, this paper ascertains the comprehensive phytochemical composition of CPEE. Also, Molecular interactions of identified compounds with cyclooxygenase (COX-2) implicated in inflammatory urpsurge is verified. GC-MS and GC-FID analyses unveil 41 phytoconstituents within CPEE. Based on the in vitro research, CPEE demonstrated potential in inhibiting thiobarbituric acid-reactive substances, nitric oxide, and phospholipase lipase A2 with inhibition rates of 2.284, 6.547, and 66.8 µg/mL respectively. In vivo experiments confirm CPEE's efficacy in inhibiting granuloma tissue formation, lipid peroxidation, and neutrophil counts compared to untreated rats. Moreover, CPEE elicited a significant (P < 0.05) increase in the activities of SOD, CAT, and GSH concentrations while decreasing C-reactive protein, signifying promising therapeutic potential. Highlighting interactions between top-scoring phytoligands (epicatechin, catechin, and kaempferol) and COX-2, the findings underscore their drug-like characteristics, favorable pharmacokinetics, and enhanced safety toxicity profiles. Results from in vitro, in vivo, and in silico studies, highlights CPEE remarkable antioxidant and anti-inflammatory potentials.

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks.

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.