Investigating the optimum parameters of a negative photoresist to prepare a V-grooved diffraction grating on Si using photolithography and reactive ion etching techniques

In this study, the deposition parameters of the SU8 2000.5 negative photoresists have been investigated and optimized for the photolithographic technique. Then, applied the inductive coupled plasma reactive ion etching (ICP RIE) to produce V-shaped groove diffraction grating on a silicon substrate. Spin coater (speed: 1000 rpm) was used to coat the photoresist over the Si substrate. The observed photoresist film thickness was measured by ellipsometry and found to be 800 nm. Interestingly, the film exhibited some stability with increasing the spin speed. The thermogravimetric analysis was used to optimize the baking temperature which was found to be ~105 °C. Contrast curves were obtained experimentally and used to optimize the exposure energy along with the images obtained from the field emission scanning electron microscopy (FESEM). The optimized energy fluence was found to be 17 mJ/cm². It was interesting to observe that the thickness of the photoresists film was increasing with the elevation of the exposure energy fluence. The FESEM images were used to optimize the ICP RIE etching process and the best etching conditions for the Si substrate were ICP power: 150 W, bias power: 100 W, and SF₆ gas flow rate: 32 SCCM (standard cubic centimeters per minute), O₂ gas flow: 8 SCCM, and Ar gas flow of 8 SCCM. It is worth to mention that well-defined V-shaped grooves were observed with a depth of 2 μm under the same experimental conditions.     

Preparing thick, defect-free films of anatase titania for dye-sensitized solar cells

Anatase titania (TiO₂) nanoparticle films were prepared on fluorine-doped tin oxide (FTO) and tin-doped indium oxide (ITO) substrates. The films were characterized by X-ray diffraction, scanning electron microscopy, profilometry, Raman spectroscopy, and optical microscopy. The results show that defects are initiated during the sintering step and continue to propagate once the film is cooled. The sintering and annealing steps were controlled by reducing the pressure and the rate of temperature change. These steps reduced the stresses generated during film preparation, allowing thick titania films on both FTO and ITO substrates to be prepared with minimal defects. Using the optimized conditions for film preparation, 20 μm thick films of titania on FTO and ITO substrates were obtained with calculated defect densities of 2.5 and 7.8%. Films as thick as 25 μm were prepared on FTO substrates with a defect density of only 6.0%. Dye-sensitized solar cells (DSSCs) were fabricated using the titania films prepared by both standard and vacuum sintering methods. DSSCs made with 20 μm titania films sintered at intermediate pressures show improvements to short-circuit current, open-circuit voltage, and device efficiency.     

Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

The most common digital media exchanged via the Internet is in text form. The Arabic language is considered one of the most sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning. In this paper, an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet. The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques. The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model. The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text. The proposed scheme is implemented using PHP with VS code IDE. The simulation results, using varying sizes of standard datasets, show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks. Comparison results with other baseline techniques show the added value of the proposed scheme.     

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.     

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices. This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification (SCADL-RWDC) method in an IoT environment. In the presented SCADL-RWDC technique, the major intention exists in recognizing and classifying ransomware attacks in the IoT platform. The SCADL-RWDC technique uses the SCA feature selection (SCA-FS) model to improve the detection rate. Besides, the SCADL-RWDC technique exploits the hybrid grey wolf optimizer (HGWO) with a gated recurrent unit (GRU) model for ransomware classification. A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique. The comparison study reported the enhancement of the SCADL-RWDC technique over other models.     

Design of a secondary standard for measuring arterial blood pressure

The measurement of blood pressure by mercury sphygmomanometer and auscultation is one of the oldest methods, and it is still widely used in health services. Nowadays, the usage of inexpensive electronic oscillometric devices for home and clinical use has increased dramatically. The purpose of this paper is to verify the reliability of such devices.     

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.     

ROS/TNF-α Crosstalk Triggers the Expression of IL-8 and MCP-1 in Human Monocytic THP-1 Cells via the NF-κB and ERK1/2 Mediated Signaling

IL-8/MCP-1 act as neutrophil/monocyte chemoattractants, respectively. Oxidative stress emerges as a key player in the pathophysiology of obesity. However, it remains unclear whether the TNF-α/oxidative stress interplay can trigger IL-8/MCP-1 expression and, if so, by which mechanism(s). IL-8/MCP-1 adipose expression was detected in lean, overweight, and obese individuals, 15 each, using immunohistochemistry. To detect the role of reactive oxygen species (ROS)/TNF-α synergy as a chemokine driver, THP-1 cells were stimulated with TNF-α, with/without H₂O₂ or hypoxia. Target gene expression was measured by qRT-PCR, proteins by flow cytometry/confocal microscopy, ROS by DCFH-DA assay, and signaling pathways by immunoblotting. IL-8/MCP-1 adipose expression was significantly higher in obese/overweight. Furthermore, IL-8/MCP-1 mRNA/protein was amplified in monocytic cells following stimulation with TNF-α in the presence of H₂O₂ or hypoxia (p ˂ 0.0001). Synergistic chemokine upregulation was related to the ROS levels, while pre-treatments with NAC suppressed this chemokine elevation (p ≤ 0.01). The ROS/TNF-α crosstalk involved upregulation of CHOP, ERN1, HIF1A, and NF-κB/ERK-1,2 mediated signaling. In conclusion, IL-8/MCP-1 adipose expression is elevated in obesity. Mechanistically, ROS/TNF-α crosstalk may drive expression of these chemokines in monocytic cells by inducing ER stress, HIF1A stabilization, and signaling via NF-κB/ERK-1,2. NAC had inhibitory effect on oxidative stress-driven IL-8/MCP-1 expression, which may have therapeutic significance regarding meta-inflammation.