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Crimes are increasing in our society as a serious worldwide issue. Fast reporting of crimes is a significantly important area in anticrime. This problem is visible in Iraq as people avoid information-sharing due to the lack of trust in the security system despite some contact lines between citizens and police in Iraq. Furthermore, there has been a little empirical study in this field. We proposed a multi-approach for crime reporting and police control to address these issues. First, this study has two goals: (1) investigating the adopted method in reporting crimes to police sectors to identify the gap and, (2) developing a mobile application for crime reporting and keeping it undisclosed and exclusive for crime witnesses to report. The approach utilised 200 participants to develop the proposed app. Results have shown that the proposed system can quickly monitor and track criminals based on a cloud-based online database. In addition, the application user will specify certain details to be sent, such as location, case type and time. Other information will be sent directly by the system following the designed algorithm.

Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain–computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time–entropy–frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis, (ii) a dynamic attribute selection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks. [ABSTRACT FROM AUTHOR]

Evolocumab, a PCSK-9 inhibitor, is known for its ability to reduce low-density lipoprotein cholesterol (LDL-C). This study aimed to investigate the effects of evolocumab, alone or in combination with atorvastatin, on the progression of atherosclerosis. Fifty male domestic rabbits were randomly assigned to five groups: control, high cholesterol diet, evolocumab vehicle (dimethyl sulfoxide, DMSO), evolocumab alone, and evolocumab plus atorvastatin. Serum levels of interleukin 10 (IL-10), IL-17, IL-1ß, intracellular adhesion molecule (ICAM), and vascular adhesion molecule (VCAM) were measured. Toll-like receptor (TLR) expression on monocytes was evaluated using flow cytometry. Histopathological examination and measurement of intimal thickness (IT) were also conducted. The results revealed that the evolocumab produced a statistically significant (p<0.05) reduction in lipid profile at 5 weeks, with the peak effect occurring at 10 weeks. Furthermore, the inhibitor reduced TLRs at 10 weeks to 10.83±1.8 and intimal thickness to 160.66±9.45. IL-17, IL-1ß, ICAM, and VCAM were significantly reduced by evolocumab treatment, with the improvement of the histopathological changes in the aortic wall. The combination of evolocumab and atorvastatin caused a more statistically significant reduction in TLRs at 10 weeks to 5.08±1.2 and intimal thickness to 121.79±5.3. IL-17, IL-1ß, ICAM, and VCAM were significantly (p<0.05) reduced by the combination, and the histopathological changes in the aortic wall were significantly improved. In conclusion, evolocumab delays the progression of atherosclerosis by modulating inflammatory pathways. [ABSTRACT FROM AUTHOR]

This research work focuses on the potential application of an organic compound, santalol, obtained from santalum album, in the inhibition of the enzyme tyrosinase, which is actively involved in the biosynthesis of melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms and significantly downgrades their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assays and enzyme kinetics to give insight into the santalol-modulated tyrosinase inhibition in a dose-dependent manner. The multi-spectroscopic techniques such as UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as a potential candidate against tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol, derived from the essential oils of santalum album, has been widely used as a remedy for skin disorders and a potion for a fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, and santalol may be employed in the agriculture, food, and cosmetic industries to prevent excess melanin formation or browning. [ABSTRACT FROM AUTHOR]