Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signa

Citric acid is an essential ingredient for the manufacture of (12) key industrial chemicals. Citric acid use is increasing steadily with a high annual growth rate as a result of the development of ever more sophisticated applications. Citric acid is widely utilized in the food and pharmaceutical industries due to its low toxicity when compared to other acidulous. Other uses for citric acid can be found in cleaning supplies and detergents. Based on information from a review of the literature, Citric acid production substrates and methods for surface fermentation, submerged fermentation, solid-state fermentation, and international market expansion are all covered in the current review study. Finally, there is still much to learn about the

Background Solar irradiance is a nonlinear and intermittent function, which makes accurate forecasting of solar power generation a challenge. The high variability of meteorological conditions is not well represented by conventional atmospheric models, thus hampering forecasting skill and model robustness. In this work, an advanced hybridization of multi-population cuckoo search (HMPCS) algorithm with machine learning (ML) methods is developed to enhance the prediction performance of photovoltaic (PV) power forecasting with more reliability. Methods In this study, a hybrid modeling framework is proposed, called HMPCS–ML framework which captures the global search capacity of HMPCS and predictive power of sophisti

Sweet pepper (Capsicum annuum L.) is an economically important vegetable crop. Wilt disease caused by Fusarium oxysporum f. sp. capsici is a specific pathogen that affects the pepper. Four isolates of F. oxysporum f. sp. capsici Fo3, Fo6, Fo7 and Fo8 were obtained from diseased pepper plants that were collected from different pepper fields in Baghdad. Fo6 isolate that has high pathogenicity to pepper seeds, Trichoderma harzianum (Th) was tested in vitro against F. oxysporum f. sp. capsici showed a high inhibition rate for the isolate Fo6, the concentration of chelated iron Fe-EDDHA 0.5% reduced the radial growth of Fo6 whi

Lower limb Rehabilitation Robots (LLRRs) assist in therapeutic tasks that involve gait recovery and joint mobility recovery of the lower limbs, in patients recovering from neurologic injuries such as stroke as well as spinal cord injury. LLRRs can sometimes be driven by preprogrammed trajectories or Inverse Kinematics (IK) trajectories, which bring increased computational demand and command supported interaction. This paper proposes an interactive control framework for LLRRs using a hybrid mix of Forward Kinematics (FK) driven movement and an offline Voice Conversational Agent (VCA), based on the Vosk speech recognition engine. The framework proposed is modular in nature that is completely “local”, running offline with no need for the I

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

Ensuring security, integrity, and reliability of the election process consider as the main challenges in the electronic voting system. This paper describes the e-voting system by integrating the biometric authentication, advanced encryption, and watermarking techniques towards meeting such challenges. The system employs the fingerprint authentication by utilizing the Scale-Invariant Feature Transform (SIFT) for verifying the identity of the voter to ensure genuineness and non-repudiation of the service. The vote will be encrypted with the AES-GCM technique to be employed in securing the voting process, thus ensuring both data privacy and integrity. Hybrid Blind Watermarking employs the technique of Discrete Wavelet Transform (DWT) a

5-Fluorouracil (5-FU) is a widely used chemotherapeutic agent, but its hepatotoxic potential poses clinical challenges, as it induces oxidative stress, inflammation, and apoptosis in liver tissue. Butein, a natural chalcone flavonoid that possesses varied biological activity, such as anticancer, anti-inflammatory, and antiplatelet effects. This study aimed to evaluate the possible protective effects of Butein against 5-FU-induced hepatotoxicity in rats. Male albino rats were divided into 4 Groups (of 7 animals each): control, 5-FU, and two Butein-pretreated Groups (50 and 100 mg/kg/day, orally for 14 days) each before a single intraperitoneal dose of 150 mg/kg 5-FU, which was injected on day 14. Serum liver enzymes (ALT and AST), cytokines

Human identification is crucial in forensics for the investigation of large-scale disasters such as fires, epidemics, earthquakes, and tsunamis. Even though biometric identification using panoramic dental radiography (PDR) has been the subject of several studies in the literature, further study remains a necessary and challenging issue. In this research, a human identification system was developed based on a convolutional neural network (CNN) and contour transform (CT). The proposed system was implemented on a total of 1540 PDR from 302 individuals. The preprocessing applied to PDRs for enhancing and taking the Region of Interest (ROI). The features were extracted using CT transform. These features were fused with features extracted