Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

The current work concerns preparing cobalt manganese ferrite (Co0.2Mn0.8Fe2O4) and decorating it with polyaniline (PAni) for supercapacitor applications. The X-ray diffraction findings (XRD) manifested a broad peak of PAni and a cubic structure of cobalt manganese ferrite with crystal sizes between 21 nm. The pictures were taken with a field emission scanning electron microscope (FE-SEM), which evidenced that the PAni has nanofibers (NFs) structures, grain size 33 – 55 nm, according to the method of preparation, where the hydrothermal method was used. The magnetic measurements (VSM) that were conducted at room temperature showed that the samples had definite magnetic properties. Additionally, it was noted that the saturation magnetizatio

The non-linear components has been emphasized for its multiple applications in rewritable recording and data storage devices. Chalcogenide glasses materials are promising due to their high refractive index. In this paper, alloys for Ge₃₀Te_70-xSb_xglasses semiconductor (where x =0.0, 5, 10, 15 and 20) will be fabricated by melt quenching method. The effect of partial substitution on DC electric power parameters, and its knowledge of electrical conduction mechanisms, were investigated to determine the effect of Antimony on the density of extended states, local states, and in Fermi energy states. The electrical measurements revealed the existence of three conduction mechanis

Microservice architecture offers many advantages, especially for business applications, due to its flexibility, expandability, and loosely coupled structure for ease of maintenance. However, there are several disadvantages that stem from the features of microservices, such as the fact that microservices are independent in nature can hinder meaningful communication and make data synchronization more challenging. This paper addresses the issues by proposing a containerized microservices in an asynchronous event-driven architecture. This architecture encloses microservices in containers and implements an event manager to keep track of all the events in an event log to reduce errors in the application. Experiment results show a decline in re

Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using M

This research examines the electrical, optical, and structural properties of zinc oxide (ZnO) and tin(IV) oxide (ZnO)1-x (SnO2)x composite thin films made by pulsed laser deposition, as well as the impact of composition concentration. The structural study of (ZnO)1-x (SnO2)x composites and thin films was conducted by X-ray analysis (XRD). Optical properties were investigated by UV–Vis infrared spectroscopy. The structural analysis revealed that all prepared thin-film composites were polycrystalline, exhibiting both hexagonal wurtzite and tetragonal phases for pure ZnO and SnO2, as well as a mixture of both phases for x=0.2 and 0.4. In contrast, the SnO2 phase was predominant for x=0.6 and 0.8. The increase in crystal size in the (

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be