Transferring data in the mobile ad hoc network can be enabled to analyze data transferring and the network that manages the data and route them into the VPN-based routing. Here is the process of maintaining the gateway for the analysis. The main problem here is the routing of the data packets, and the analysis of the nodes in the form of packages is the main issue in this study. To fix this, troubleshooting problems can be enabled for the packets which reach the destinations and the echo response. The primary technique used in this study is energy efficient geographic routing protocol and reward-based intelligent Ad hoc routing is used for the analysis. The energy-efficient geographic routing protocol enables the EGRPM method to reduce the sensor nodes and the WSN. This allows gathering the data and the nodes to maintain the geographic way. Reward-based intelligent Ad hoc routing is used in automatic decision-making, and the analysis of the system to produce the selection action for the research is reinforcement learning. This results from the study of the configuration and the analysis of the data in the ad hoc network. This enables the formation of learning about the routing protocol and facilitates the current data transfer to the research done in the ad hoc networks. This data analysis in the mobile network helps analyze the system and the entire data management.
Precision agriculture, driven by advancements in machine learning (ML) and the Internet of Things (IoT), has revolutionized modern crop farming by enabling real-time monitoring, predictive analytics, and data-driven decision-making. Despite the growing integration of machine learning and IoT in precision agriculture, challenges such as data heterogeneity, sensor reliability, computational complexity, and cybersecurity continue to hinder optimal system performance. This paper addresses the need for robust, scalable, and secure ML-IoT frameworks to enhance real-time decision-making and sustainability in modern farming. This review explores the integration of ML techniques with IoT-based precision agriculture systems to enhance crop health monitoring, soil analysis, irrigation management, and yield prediction. Various ML algorithms have been extensively utilized for disease detection, nutrient optimization, and climate impact assessment. This paper examines the role of IoT sensors in collecting real-time data, such as temperature, soil moisture, and humidity, to facilitate precision farming. Furthermore, this study highlights challenges such as data heterogeneity, sensor reliability, computational complexity, and cybersecurity threats in ML-driven IoT frameworks. A comparative analysis of existing ML models, their accuracy, scalability, and computational efficiency is provided to evaluate their effectiveness in precision agriculture applications. Additionally, this review discusses the integration of edge computing and cloud-based architectures for optimizing data processing and decision support in smart farming. Future research directions focus on the development of hybrid ML models, explainable AI techniques, and blockchain-based secure data sharing for sustainable and scalable precision agriculture solutions.
This work investigates the mixed-mode (I/II) fracture behaviour of banana fiber-reinforced epoxy composite using asymmetric semi-circular bend (ASCB) specimens. Laminates were fabricated via hand layup-assisted vacuum bag moulding, with banana fibers integrated into an epoxy matrix. Three-point bending tests were conducted on ASCB specimens (radius 60 mm, thickness 6 mm, and notch length 30 mm) under varying asymmetric support spans (40-40 mm to 40-20 mm) to transition from pure mode Ito mixed-mode I/II loading. Results showed a non-monotonic mode I fracture toughness (KI) trend (1.70 MPa.m1/2 decreasing to 1.54 MPa. m1/2, then increasing to 1.92 MPa.m1/2) and rising mode II fracture toughness (KII) (0.30 to 0.87 MPa.m1/2) with increasing mode II contribution, attributed to fiber-matrix interactions. Experimental results were compared with analytical predictions based on the Power Law, maximum tangential stress (MTS), generalised maximum tangential stress (GMTS), and maximum energy release rate (Gmax) criteria. The Power Law criterion underestimated fracture resistance failing to capture constraint effects and fiber-related toughening. Incorporation of T-stress in the MTS and GMTS models improved predictions, though both overestimated crack initiation angles. Among the compared criteria, Gmax provided the closest correlation with crack initiation angles (0 degrees to-17.26 degrees), highlighting its suitability for natural fiber composites. Overall, the findings reveal a strong dependence of mixed-mode fracture behaviour on fiber-matrix interaction mechanisms, confirming the suitability of energy-based criteria for accurately modelling the fracture response of anisotropic natural fiber composites.
Plant-based therapeutics are increasingly studied as alternatives or add-ons to conventional antiepileptic medications. Bauhinia variegata is a medicinal plant used in traditional medicine and is reported to exhibit antioxidant and neuroprotective properties. In this study, fresh leaves of B. variegata were collected, authenticated, shade-dried, coarsely powdered, and extracted using a hydroalcoholic solvent system (ethanol:water, 6:4) via Soxhlet extraction, then dried under vacuum and lyophilized. The hydroalcoholic extract of B. variegata leaves was prepared and used for qualitative phytochemical tests and GC–MS analysis to identify major bioactive compounds. Anticonvulsant activity was tested in rats with the MES model, where animals received oral doses of the extract before electroshock-induced seizures. Preliminary phytochemical screening showed the presence of terpenoids, steroids, fatty acids, and antioxidant constituents. In the MES model, administering the hydroalcoholic extract significantly reduced seizure severity and/or prevented hind-limb tonic extension compared with vehicle-treated controls, indicating effectiveness against generalized tonic-clonic seizures. The hydroalcoholic extract of Bauhinia variegata leaves shows promising antiepileptic activity in the MES rat model, likely due to its antioxidant and neuroprotective phytoconstituents. Further research is needed to isolate active compounds, understand mechanisms (such as antioxidant activity and ion-channel modulation), and evaluate safety and dose-response relationships.
There are serious health and environmental hazards when toxic dyes from the textile industry are dumped into waterways. These dyes were not removed by conventional adsorbents. Therefore, the current study offers a novel method for creating environmentally friendly iron oxide nanoparticles using sapota leaves extract. The physical and chemical characteristics of iron oxide nanoparticles were analysed by utilizing a variety of instrumental techniques. Batch adsorption studies revealed the ideal conditions for eliminating Crystal Violet (C25 H 30 N 3 Cl) dye. With a pH of 10, 0.5 g of nanoparticles, 60 min of reaction time, 30 mg/L of dye, and 303 K of reaction temperature. The environmentally friendly iron oxide nanoparticles demonstrated successful removal of 93.90 % of Crystal Violet (CV) dye in the aqueous solution and the computed equilibrium adsorption capacity value (Qe) was 2.817 mg/g at optimal conditions. Isotherm studies showed that, the Langmuir isotherm model described the adsorption of crystal violet dye by environmentally benign iron oxide nanoparticles. Thermodynamic studies revealed that the adsorption process was exothermic and spontaneous. The adsorption of crystal violet dye onto environmentally friendly iron oxide nanoparticles was described by kinetic parameters that were determined to be a pseudo-second-order model (R2 = 0.9996). The CV adsorption methods suggested surface complexation, cation it-it interaction, hydrogen bonding, electrostatic interaction, and physical adsorption through various internal and surface moieties. Additionally, Response Surface Methodology (RSM) was assessed, and it was shown that while higher temperatures will negatively affect % removal, the low temperature of 303 K is advantageous for % removal. The outcomes of the RSM model are consistent with the observations from the experiments. To analyze the associated adsorption mechanism, theoretically sophisticated models such as Monte Carlo (MC) simulation, fractional free volume (FFV), adsorption loading and isotherm were employed.
