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Effects of Particle Size and Mixture Composition on Propulsive Performance of Gelled and Refrigerated Aluminum-Liquid Oxidizer Propellants
No full textThe propulsive performance of aluminum-liquid oxidizer mixtures is studied using an Eulerian-Eulerian multiphase Computational Fluid Dynamics model. The simulations are performed to capture the high-speed multiphase flow dynamics in a lab-scale rocket motor and predict the propulsive performance parameters. The focus of the study is to examine the effects of particle size and mixture composition on key performance parameters such as chamber pressure, thrust, characteristic velocity, and specific impulse. For nano-aluminum and water propellants, the chamber pressure and thrust increase significantly with decreasing particle diameter; a d−1.6 p correlation is observed. The specific impulse increases with decreasing particle size due to the reduction in two-phase flow losses. However, when the particle size is decreased below 50 nm, a reduction in the specific impulse is observed due to the increase in the oxide content in the particles. The effect of mixture composition is studied by varying the equivalence ratio and H2O2 concentration in the oxidizer. Decreasing the equivalence ratio from 0.943 to 0.71 and increasing the H2O2 concentration in the oxidizer from 0 to 25%increased the peak chamber pressure and peak thrust due to the increase in the propellant burning rates. However, the characteristic velocity and specific impulse are not as strongly affected. Modifying the pH of water also increases the peak chamber pressure and peak thrust without altering the specific impulse significantly. Overall, the computed specific impulses are in the range of 130-175 s, substantially lower than the ideal theoretical specific impulse, thereby highlighting excessive two-phase flow losses for these propellants
Intelligent Data Dissemination in Vehicular Networks: Leveraging Reinforcement Learning
No full textData dissemination in Vehicular Ad Hoc Networks (VANETs) is vital for the development and operation of intelligent transportation systems, as it enables the rapid and reliable exchange of critical information among vehicles and infrastructure. However, the dynamic nature of VANETs, characterised by high node mobility and frequently changing network topologies, poses significant challenges for conventional routing protocols. The major challenges of traditional routing protocols struggle with scalability, Quality of Service (QoS), and efficient data dissemination. Machine learning (ML) based traditional routing algorithms that typically rely on predefined datasets for training and can struggle to adapt to the dynamic and unpredictable nature of VANET environments. In contrast, reinforcement learning (RL) excels by learning from interactions with the environment in real-time. RL-based routing algorithms can adaptively optimize routing decisions based on the constantly changing network conditions, such as vehicle density, mobility patterns, and communication link quality. This chapter explores the potential of Reinforcement Learning (RL) to address these challenges by enabling adaptive routing protocols that dynamically adjust to network conditions. We provide a comprehensive overview of the fundamentals of RL and examine how these concepts can be applied to develop RL-based routing strategies in VANETs. Through detailed analysis and discussion, the chapter demonstrates the ability of RL to enhance the scalability, QoS, and overall performance of data dissemination in VANETs, paving the way for more robust and efficient vehicular communications in future ITS deployments
Worst-Case Response Time Analysis for Periodic Programs with Nested Locks
No full textPeriodic applications include tasks that run continuously over certain intervals, which can lead to data races with concurrency. Locks have been traditionally used as a synchronization mechanism to ensure correctness. However, it is possible that a low priority task gets preempted for a high priority task while holding a lock that the high priority task requires. In safety-critical systems, this can result in undesirable situations where high priority tasks have to wait for low priority tasks to complete, violating its periodicity and priority. While prior works have proposed techniques for race detection in applications without nested locking, these techniques do not generalize to applications using nested locks. In this work, we present a worst-case response time analysis and a sound technique for static race detection in the context of periodic applications having nested locking behavior. Our algorithm offers a conservative upper bound for task response times when dealing with periodicity and addressing the complexities introduced by nested locks. Our approach improves the safety and dependability of concurrent periodic programs with nested locks. We implement our algorithm in Rust, and evaluate its performance and correctness on a set of programs
Public transportation-based evacuation planning for the statue of unity using microscale traffic modelling
No full textZingerone nanoparticle and laponite–embedded natural gum based injectable hydrogel for fast track wound repair
No full textCurrently, the treatment modalities for hard-to-heal deep skin wounds are limited by bacterial growth, excessive wound exudate, elevated reactive oxygen species (ROS), and extensive tissue damage. This study reports a multifunctional hydrogel composite with intrinsic antibacterial, antioxidant, and antibiofilm properties. The hydrogel is a physically crosslinked matrix with natural gums: guar gum (GG), tragacanth gum (TG), dual charged laponite nanoclay and polyethyleneimine (PEI) as a cross-linker. GG and TG are physically crosslinked and provide a moist environment through their high swelling capacity and controlled drug release properties. Laponite and PEI enhance the hydrogel's stability by crosslinking with GG and TG through electrostatic interactions and non-covalent bonding, in addition to providing intrinsic antioxidant, antibacterial and antibiofilm properties. The whole hydrogel matrix is further infused with zingerone nanoparticles (Z NPs), which exhibit potent antibacterial and antioxidant properties, further boosting its therapeutic efficacy. In vivo studies demonstrate that hydrogel significantly accelerates wound healing by promoting re-epithelialization, and the formation of apocrine glands compared to positive controls. This synergistic combination has resulted in the development of a stable, injectable nanocomposite hydrogel with superior therapeutic and bio-adhesive capabilities, making it highly suitable for integration with medical devices and providing a promising solution for effectively treating deep skin wounds and minimizing complications
An alternate perspective on the critical orientation of horizontal pair of seismic excitations: Theory and illustrations
No full textThis paper enhances the understanding of critical orientation (that maximizes the response of an EDP over all possible orientations) for horizontal ground motion pairs when applied to the base of a structure. Given that any structural response comprises the contributions from individual modes, even under inelastic excursion, the maximization of modal responses for the first few modes would be sufficient to define the critical orientation for most of the EDPs. In line with this, the theoretical background to establish a concept of modal orientation is presented in this paper. The analysis for the modal response of a building against a pair of orthogonal horizontal excitations is theoretically equivalent to the analysis against the associated single component representation along this orientation. The orientation of the same single-component representation that maximizes the associated oscillator's response is termed as the spectral orientation. The critical orientation is proven to be the difference between the modal and spectral orientations. In line with this, a recommendation is proposed for critical orientation regardless of the EDPs, which is expected to capture most of the EDPs, if not all. Although this development is based on linear analysis, it is demonstrated to be applicable to buildings undergoing inelastic excursion under moderate-to-strong shaking. These recommendations are based on four numerical examples involving symmetric and unsymmetric buildings subjected to individual horizontal pairs of ground motions and also a suite of ground motions. The proposed recommendations on critical orientation should be incorporated into seismic design and performance assessment practices
Analysis and Design of Approximate Inner-Product Architectures Based on Distributed Arithmetic
No full textDistributed arithmetic (DA) based architectures are popularly used for inner-product computation in various applications. Existing literature shows that the use of approximate DA-architectures in error resilient applications provides a significant improvement in the overall efficiency of the system. Based on precise error analysis, we find that the existing methods introduce large truncation error in the computation of the final inner-product. Therefore, to have a suitable trade-off between the overall hardware complexity and truncation error, a weight-dependent truncation approach is proposed in this paper. The overall efficiency of the structure is further enhanced by incorporating an input truncation strategy in the proposed method. It is observed that the area, time and energy efficiency of the proposed designs are superior to the existing designs with significantly lower truncation error. Evaluation in the case of noisy image smoothing application is also shown in this paper
Deep Echo State Networks for Detecting Internet Worm and Ransomware Attacks
No full textWith the advancement of technology over the last decade, there has been a rapid increase in the number and types of malware attacks such as worms whose primary function is to self-replicate and infect systems and ransomware that corrupts and encrypts data. Developing proactive cyber defense techniques is essential for effectively detecting network anomalies that are evolving and becoming more challenging to identify. In this paper, we consider intrusion detection techniques using fast machine learning algorithms. We investigate Echo and Deep Echo State Networks machine learning structures for detecting worm and ransomware anomalies. We demonstrate, analyze, and compare merits of this approach using Slammer worm, WannaCrypt ransomware, and WestRock ransomware attack datasets. � 2023 Elsevier B.V., All rights reserved
Does form entail function? understanding pottery functionalities through absorbed residues within ceramics from the Harappan sites of Karanpura and Ropar in India
No full textThe Harappan Culture emerged in the Indus-Saraswati Region, with sites spreading over present-day northwest India, Gujarat, Pakistan, and Afghanistan between 2600 and 1900 BCE. Harappan sites have yielded extensive structural and material culture discoveries and diverse pottery assemblages, making it clear that it was an important and complex urban society extending over a wide area for more than 700 years. Harappan pottery, well-studied by various scholars, offers valuable insights into the socio-cultural-economic developments of its manufacturers and users during the culture’s existence. The current research directly determines the function of 15 potsherds scientifically from excavated Harappan sites of Karanpura and Ropar in Rajasthan and Punjab, respectively, in India. Besides the usual forms like cooking pots, the study explores unique ones like perforated jars and incised pottery. The samples are typical Harappan pottery types found in most settlements, both in habitation and burial contexts. Interestingly, perforated jars in burials are always associated with a wide-mouthed pot. The inclusion of these two vessel types in association is characteristic of the Harappan burials at other sites like Harappa and Kalibangan. The previous studies on Harappan ceramic types have mainly focused on their typological and morphological attributes, sometimes substantiated through ethnographic analogies. Until recently, only a few pilot studies employed organic residue analysis techniques to examine the relationship between the pottery forms and their possible functions by evaluating the lipids absorbed within their porous matrices (rim, body, and base). From the methodological perspective, this research examines the type and amount of lipids preserved within the sampled sherds, the potential effect of porosity on residue preservation, and possible contaminants affecting the interpretation of these lipids. This study focuses on studying the function of vessels using the direct scientific determination of vessel contents through residue analysis, moving away from ceramic research grounded solely on conventional forms found in the excavated settlements