Effect of neodymium ion on the structural,electrical and magnetic properties of nanocrystalline nickel ferrites

NiNd_xFe_2-xO₄ nanoferrites with different compositions of x?=?0.01, 0.03, 0.05, 0.07 and 0.09 were prepared using the sonochemical method. The structural, optical and morphological properties of the prepared nanoferrites were characterized by X-ray diffraction, ultra violet-diffuse reflectance spectroscopy, scanning electron microscopy and X-ray fluorescence techniques. The X-ray diffraction analysis of the prepared nanoferrites confirmed the presence of a cubic spinel structure. The average crystallite sizes of the prepared nanoferrites were 52, 49, 46, 44 and 40?nm for x?=?0.01, 0.03, 0.05, 0.07 and 0.09, respectively. The particle size of the prepared NiNd_xFe_2-xO₄ nanoferrites was in the range 60–40?nm. The dielectric parameters ranged from 2.9?GHz to 5.6?GHz. Decrease in the dielectric constant was observed with an increase in Nd³⁺ ions in the prepared NiNd_xFe_2-xO₄ nanoferrites. However, a reverse trend was observed in the dielectric loss. An impedance analysis of the prepared nanoferrites was carried out to explore the pseudo-capacitance behavior. The saturation magnetization and remnant magnetization values of the prepared nanoferrites decreased with an increase in the concentration of Nd³⁺ ions in NiNd_xFe_2-xO₄ nanoferrites.     

Evaluation of usage and fuel savings of solar ovens in Nicaragua

Solar cooking technology has been promoted as a solution to both global poverty and environmental degradation, but relatively little research exists on the impact of solar oven usage on biomass fuel consumption. This study evaluates solar oven usage and wood consumption in northern Nicaragua during both the rainy and dry seasons, using surveys, temperature dataloggers, and direct measurements of fuelwood use. Solar oven owners reported usage on 79% of days during the dry season, and 41% of days during the rainy season. Comparison with oven temperature records confirmed usage on 50% of days during the dry season, and 16% of days during the rainy season. However, wood consumption measurements showed no statistically significant difference between days with solar oven usage and days without, suggesting that frequency of usage alone is not an appropriate proxy for fuel savings. Survey results suggest that a large part of solar oven usage came in addition to biomass cooking, as opposed to replacing it. These results suggest a need for further study of wood consumption in situ and more focus on the specific kinds of foods prepared in solar cookers, as well as local cultural and climatic conditions.     

A Systematic Review on Techniques Adapted for Segmentation and Classification of Ischemic Stroke Lesions from Brain MR Images

A life threatening medical condition occurs when arteries that supplies blood to the brain gets blocked resulting in Ischemic Stroke. Magnetic resonance imaging (MRI) plays major role in diagnosis of brain stroke at early stages. Manual detection of stroke lesions by medical experts is time-consuming, expensive, and susceptible to intra- and inter-observer variability. Accurate detection of stroke lesions from brain MRI, the challenging task requires development of automated computer aided diagnostic techniques. This paper aims at reviewing the state of art techniques currently available fulfilling the above objectives, their merits and limitation. Through this review we figure out the modifications that need to be carried out in future to develop best automated diagnostic tool which performs better and mitigates all the pitfalls in current literatures.

     

Trust-based Intelligent Scheme for Mitigating Black Hole Attacks in IoT

Internet of Things (IoT) networks are characterized by a multitude of wireless, interconnected devices that can dynamically join or exit the network without centralized administration or fixed infrastructure for routing. While multipath routing in IoT networks can improve data transmission reliability and load balancing by establishing multiple paths between source and destination nodes, these networks are susceptible to security threats due to their wireless nature. Traditional security solutions developed for conventional networks are often ill-suited to the unique challenges posed by IoT environments. In response to these challenges, this paper proposes the integration of the Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol with a trust model to enhance network performance. Key findings from this research demonstrate the successful fusion of AOMDV with a trust model, resulting in tangible improvements in network performance. The assessment of trustworthiness bolsters both security and routing capabilities in IoT networks. The trust model plays a crucial role in mitigating black hole attacks in IoT networks by evaluating the trustworthiness of nodes and helping in the identification and avoidance of malicious nodes that may act as black holes. Simulation results validate the efficacy of the proposed trust-based routing mechanism in achieving its objectives. Trust plays a pivotal role in decision-making and in the creation of secure distribution systems. By assessing the trustworthiness of nodes, both network security and routing efficiency can be enhanced. The effectiveness of the proposed trust-based routing mechanism is scrutinized through simulations, offering insights into its potential advantages in terms of improved network security and routing performance in the context of the IoT.