Abstract Introduction: MMP3 plays a crucial role in the process of bone erosion in the pathomechanism of rheumatoid arthritis (RA). It acts by removing the outer osteoid layer, which allows the osteoclasts to tightly connect and carry out the subsequent damage to the underlying bone. MMP3 can trigger the production of other MMPs like MMP-1, MMP-7, and MMP-9, it plays a pivotal role in the remodeling of connective tissues. Aim of the study: to assess the influence of MMP-3 serum levels and single-nucleotide polymorphisms of rs679620 in the rheumatoid arthritis patients' group in comparison to the control group. Subjects: eighty eight samples, 45 rheumatoid arthritis patients after being referred by their treating physician for regular RA

The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present s

Measuring the efficiency of postgraduate and undergraduate programs is one of the essential elements in educational process. In this study, colleges of Baghdad University and data for the academic year (2011-2012) have been chosen to measure the relative efficiencies of postgraduate and undergraduate programs in terms of their inputs and outputs. A relevant method to conduct the analysis of this data is Data Envelopment Analysis (DEA). The effect of academic staff to the number of enrolled and alumni students to the postgraduate and undergraduate programs are the main focus of the study.

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

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 Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Activated carbon (AC) is a highly important adsorbent material, as it is a solid form of pure carbon that boasts a porous structure and a large surface area, making it effective for capturing pollutants. Thanks to its exceptional features, AC is widely used for purifying water that is contaminated with odors and removing dyes in a cost-effective manner. A variety of carbonic materials have been employed to prepare AC, and this study aimed to evaluate the suitability of utilizing waste mango and avocado seeds for this purpose, followed by testing their efficacy in removing dye from aqueous solutions. The results indicate that using waste mango and avocado as AC is technically feasible, achieving dye removal percentages of 98% and 93%,