Electronic Health Record (EHR) systems are used as an efficient and effective method of exchanging patients’ health information with doctors and other key stakeholders in the health sector to obtain improved patient treatment decisions and diagnoses. As a result, questions regarding the security of sensitive user data are highlighted. To encourage people to move their sensitive health records to cloud networks, a secure authentication and access control mechanism that protects users’ data should be established. Furthermore, authentication and access control schemes are essential in the protection of health data, as numerous responsibilities exist to ensure security and privacy in a network. So, the main goal of our s

This paper presents designing an adaptive state feedback controller (ASFC) for a magnetic levitation system (MLS), which is an unstable system and has high nonlinearity and represents a challenging control problem. First, a nonadaptive state feedback controller (SFC) is designed by linearization about a selected equilibrium point and designing a SFC by pole-placement method to achieve maximum overshoot of 1.5% and settling time of 1s (5% criterion). When the operating point changes, the designed controller can no longer achieve the design specifications, since it is designed based on a linearization about a different operating point. This gives rise to utilizing the adaptive control scheme to parameterize the state feedback controll

This study can be considered as un introduction to the idioms and the strategy about the productive partnership development connection, that helps the researcher and the organization in their work in to activate development and the natural sources,manegment,to improve the two sides active connected to the local society, and to use it as easier and smoother participation of the people who work in development field and the natural sources management research, That connection depends mostly upon the capability of researcher and the development worker to increase the ability of individual and local society to specify and analyzing their problems and to try solutions to make their life better with good income.   

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

If feminist philosophy in the context of feminist research focuses on how to produce an alternative knowledge and culture for women and forming anew awareness of their roles in the face of prevailing misconceptions then the topic of Islamic feminism is presented as a philosophical topic in the field of human knowledge to discuss how to produce an alternative knowledge of traditional knowledge prevailing in patriarchal societies to restore the balance of power and authority in the relationship between the sexes to create an effective feminist role in advocating for and defending womenś issues to achieve this Islamic feminism sought to establish an Islamic epistemology .

The work reported in this study focusing on the abrasive wear behavior for three types of pipes used in oil industries (Carbone steel, Alloy steel and Stainless steel) using a wear apparatus for dry and wet tests, manufactured according to ASTM G65. Silica sand with
hardness (1000-1100) HV was used as abrasive material. The abrasive wear of these pipes has been measured experimentally by measuring the wear rate for each case under different sliding speeds, applied loads, and sand conditions (dry or wet). All tests have been conducted using sand of particle size (200-425) µm, ambient temperature of 34.5 °C and humidity 22% (Lab conditions).
The results show that the material loss due to abrasive wear increased monotonically with

Recently emerging pandemic SARS CoV-2 conquered our world since December 2019. Continuous efforts have been done to find out effective immunization and precise treatment stetratigies A way from therapeutic options that were tried in SARS CoV-2, an increased attention is directed to predict natural products and mainly phytochemicals as collaborative measures for this crisis. In this review, most of the mentioned compounds specially flavonoids (biacalin, hesperidin, quercetin, luteolin,, and phenolic (resveratrol, curcumin, and theaflavin) exert their effect through interfering with the action of one or more of this proteins (spike protein, papain like protease, 3 chymotrypsin like cysteine protease, and RNA dependent RNA 

The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob