Emulsion Treated Base (ETB) is a base stabilization technique in which aggregates are coated with a bituminous emulsion, imparting flexibility to the material. In conventional pavement design, ETB is assumed to fail primarily through permanent deformation, and therefore the provision of a crack relief layer is generally not considered necessary. However, when designers have flexibility in selecting the proportions of constituent materials, certain ETB combinations may exhibit a tendency to fail through cracking rather than rutting. This study evaluates the factors influencing the cracking resistance of ETB mixes using fracture energy and post-peak slope as key performance indicators. Results indicate that fracture energy increases with higher emulsion content, active filler content, incorporation of Reclaimed Asphalt Pavement material (RAP) and greater compaction effort, suggesting improved particle bonding. Conversely, the post-peak slope increases with higher cement content and compaction effort, indicating a greater susceptibility to cracking. It was observed that ETB mixes with 50
The oxidation kinetic behavior of the Haynes-230 alloy used in hot combustion components for very long-term high-temperature oxidation behavior is important for elevated temperature usage of Haynes-230 alloy. In situ oxidation behavior of Haynes-230 alloys subjected to high-temperature (800, 1000, and 1200 °C) exposures in a customized furnace setup equipped with electrochemical impedance spectroscopy (EIS) is investigated. Phase analysis, surface morphology, and chemical composition of the heat-treated samples were evaluated using x-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive x-ray spectroscopy (EDS). The various forms of surface oxide layer formation were also investigated using Raman spectroscopy. The EIS method was also utilized to evaluate the oxidation behavior of Haynes-230 at various temperatures and time durations. Furthermore, the oxidation kinetics of the alloy were investigated under thermal loading and unloading conditions, while the capacitive behavior was observed under unloading conditions, and a maximum charge transfer resistance is 27.83 × 106 observed at 1200 °C-24 h with activation energy of 182 kJ/mol.
Superalloys have played a prominent role, particularly in aerospace applications, for the past few decades. The present investigation examines the oxidation resistance of Ni-based superalloys at 1200 °C under isothermal high-temperature environment. The oxidation resistance and kinetics of three distinct Ni-based superalloys—Inconel-718, Haynes-230, and XH60BT—were investigated using electrochemical impedance spectroscopy (EIS). A detailed understanding of oxidation kinetics was explored by phase and chemical analysis using x-ray diffraction and x-ray photoelectron spectroscopy (XPS). The surface morphology of oxide products, including tetragonal, biaxial pyramids, flakes, and polygonal plates, was identified using scanning electron microscopy (SEM). The growth of oxide layer over time was studied by energy-dispersive x-ray spectroscopy (EDS), and mechanical property is analyzed using of Vickers hardness testing. According to the EIS data, the charge transfer resistance (Rct) values vary over time based on the alloy’s composition and oxide growth kinetics. The highest Rct is observed in Inconel-718 as 206 MΩ as oxide layer growth and highest oxidation resistance is observed in XH60BT as lowest Rct with small variation in the mechanical strength due to high-temperature oxidation HVN in range of 272-227.
Simple prediction of food composition and related properties could reduce reliance on complicated measurement methods that use costly instruments and chemicals. In this study, low-field nuclear magnetic resonance (LF-NMR) relaxation measurements (T1 and T2) were used. Nine batches of date fruits (DFs) differing in quality were analyzed using relaxation descriptors extracted to characterize proton environments associated with different mobility states, with transverse (T₂) relaxation reflecting contributions from rigid, semirigid, and mobile proton populations. Descriptors associated with rigid, semirigid, and mobile proton populations were obtained using transverse (T2) relaxation. The relationships between these parameters and the physicochemical characteristics of the samples of date fruit were analyzed. LF-NMR T1 and T2 parameters showed linear correlations with studied physicochemical properties, including moisture, crude fiber, and pectin. This indicates the potential of LF-NMR for predicting physicochemical properties of date fruits. Multivariate analysis was used to examine clustering trends among date fruits based on LF-NMR relaxation behaviors. The decision tree model achieved high predictive performance for moisture content (R2 = 0.990) using T₂ relaxation parameters. Artificial neural networks considering T2 relaxation also showed strong internal predictive performance for sucrose (R2 = 0.998) and fiber (R2 = 1.000). These results demonstrate that integrating LF-NMR relaxation with multivariate and machine learning approaches provides an effective framework for predicting key physicochemical attributes of date fruits.
Mobile Ad hoc Networks (MANETs) are distributed and infrastructure-less wireless networks that are prone to various security threats due to their dynamic topology, open medium, and lack of centralized device for control. Existing security mechanisms are often deficient in detecting sophisticated or evolving attacks. This article presents a method to detect Man in middle attack through the integration of Random Forest based Intrusion Detection Systems (IDS). Random forest can effectively inspect network traffic patterns, identify anomalies, and detect harmful activities with high precision. The proposed system leverages real-time traffic monitoring, automated feature extraction, and adaptive learning to improve detection rates which may reduce false positives. This method also discusses challenges such as computational overhead, data scarcity, and the need for lightweight models suitable for mobile environments. The integration of random forest into MANET security frameworks offers a promising direction for establishing an intelligent, self-adaptive, and robust intrusion identification system in dynamic wireless scenario.
