How was the SME’s Performance in Oman during Covid-19?

The paper examines the performance and the challenges faced by SMEs in Oman during Covid-19. The data was collected through primary and secondary sources. A questionnaire of 100 respondents was used. Descriptive analysis and chi-squared test have been employed in the data analysis. The main findings of the study showed despite the adverse effect of the pandame, the  SMEs in Oman have performed well and indicated a resilient and adaptable strategy to cope wth the virus. The reason for this could be attributed to their ability to quickly transition to remote work and online operations. During such a challenging period, some businesses managed to thrive due to their ability to identify opportunities and leverage them. Moreover, the study revealed that financial stability and preparedness are key to navigating the pandemic's uncertainties, as businesses focus on risk management, budget planning, and saving money. In response to unexpected disruptions, businesses have become more cautious and strategic in their financial decisions. SME resilience against future crises is enhanced as a result of SMEs adopting more prudent financial practices. The key  challenges faced by SMEs during Covid-19 included delivery delays, reduced income for consumers, and regulation changes. The SMEs in Oman devised severalrisk management strategies, such as maintaining backup plans and setting aside funds, reflect a forward-thinking approach. The findings indicate that businesses in Oman are actively seeking ways to minimize disruptions to their operations and mitigate potential risks. Long-term resilience can be built by such strategic thinking

Data hiding using integer lifting wavelet transform and DNA computing

DNA computing widely used in encryption or hiding the data. Many researchers have proposed many developments of encryption and hiding algorithms based on DNA sequence to provide new algorithms. In this paper data hiding using integer lifting wavelet transform based on DNA computing is presented. The transform is applied on blue channel of the cover image. The DNA encoding used to encode the two most significant bits of LL sub-band. The produced DNA sequence used for two purpose, firstly, it use to construct the key for encryption the secret data and secondly to select the pixels in HL, LH, HH sub-bands for hiding in them. Many measurement parameters used to evaluate the performance of the proposed method such PSNR, MSE, and SSIM. The experimental results show high performance with respect to different embedding rate

Generating and Validating DSA Private Keys from Online Face Images for Digital Signatures

Signing digital documents is attracting more attention in recent years, according to the rapidly growing number of digital documents being exchanged online. The digital signature proves the authenticity of the document and the sender’s approval on the contents of the document. However, storing the private keys of users for digital signing imposes threats toward gaining unauthorized access, which can result in producing false signatures. Thus, in this paper, a novel approach is proposed to extract the private component of the key used to produce the digital signature from online face image. Hence, this private component is never stored in any database, so that, false signatures cannot be produced and the sender’s approval cannot be denied. The proposed method uses a convolutional neural network that is trained using a semi-supervised approach, so that, the values used for the training are extracted based on the predictions of the neural network. To avoid the need for training a complex neural network, the proposed neural network makes use of existing pretrained neural networks, that already have the knowledge about the distinctive features in the faces. The use of the MTCNN for face detection and Facenet for face recognition, in addition to the proposed neural network, to achieved the best performance. The performance of the proposed method is evaluated using the Colored FERET Faces Database Version 2 and has achieved robustness rate of 13.48% and uniqueness of 100%

Optimization and fault diagnosis of 132 kV substation low-voltage system using electrical transient analyzer program

In this paper, a simulation and analysis of 132 kV Substation in feeds western Iraq have been presented including a short circuit (S.C) analysis. This work helps to properly control and coordinate the protection equipment used in this grid interconnection spot. This work includes power flow analysis carried out using electrical transient analyzer program (ETAP) simulator. Also, the most common types of faults are investigated for the substation buses using International Electrotechnical Commission (IEC) and the American National Standards Institute (ANSI) standards to discover the behavioral characteristics under different scenarios for the substation transformers connection to assess the range of S.C current this substitution can ride through. Finally, the results of ANSI and IEC are theoretically investigated for validity to ensure reliability and quality assurance in the case study substation

Hypocone Reduction and Carabelli’s Traits in Contemporary Jordanians and the Association between Carabelli’s Trait and the Dimensions of the Maxillary First Permanent Molar

The objective of this study is to determine the prevalence of expression and bilateralism of two dental morphological traits in contemporary Jordanians: The hypocone reduction trait on the maxillary second permanent molar and Carabelli’s trait on maxillary permanent first and second molars. Furthermore, inter-trait correlation and the relationship of Carabelli’s traits with upper first molar dimensions were investigated. Three hundred subjects of school children at their 10th grade and of an average age of 15.5±0.4 years were involved. Alginate impressions for the maxillary arch were taken, dental casts were reproduced. The selected accurate casts were of 132 male- and 155 female-students. The frequencies of hypocone reduction trait on the maxillary second molar and Carabelli’s trait on the maxillary molars were examined. Buccolingual and mesiodistal diameters of the maxillary first molar were measured and recorded. Paired Sample t test and Nonparametric Correlation analysis were used for data analysis. Hypocone reduction trait on the maxillary second molar was found in 29.8 % of the examined students. Positive forms of Carabelli’s trait on first and second molars were observed in 65.0 % and 3.8 %, respectively. Nonparametric correlation analysis revealed positive association between Carabelli’s trait on first molar and hypocone reduction trait on the maxillary second molar. The presence of Carabelli’s trait on first molar was strongly associated with the increase of buccolingual, but not the mesiodistal, diameter. Bilateralism was found highly significant in the tested traits and both genders (p<0.001). This finding might be a sign of relatively low environmental stresses in the living Jordanian population and/or great ability of its individuals to buffer the adverse effects of such stresses

Enhancing Power Plants Safety by Accurately Predicting CO and NOx Leakages from Gas Turbines Using FFNN and LSTM Neural Networks

Gas power plants are fast-establishing power plants capable of producing reliable energy in high watts volumes. One of its significant features is its dependency on natural air as raw material to run the gas turbine. Air passes through several stages that involve heating the air to increase its pressure before being used in electric power generation. Leakage in gas power stations is considered a vital indication of irregular processes of those stages. Any fault existing in the meanwhile operations can result in lousy production performance. Considering the human and economic losses of gas leakage, it has become a challenge to prevent the same. One of the essential approaches to managing gas leakage reduction is an accurate prediction. This paper proposes an automatic prevention approach relying on deep learning technology for predicting gas leakage status. Furthermore, a novel dataset was supplied by a natural gas power plant to predict CO and NOx emissions. The dataset is used to train the deep learning models using Long-short Term Memory and Feed-Forward Neural Networks. The optimum accuracy obtained is over 92% for CO and over 58% for NOx while using the LSTM model as a predictor

Electrical characterization of si nanowire GAA-TFET based on dimensions downscaling

This research paper explains the effect of the dimensions of Gate-all-around Si nanowire tunneling field effect transistor (GAA Si-NW TFET) on ON/OFF current ratio, drain induces barrier lowering (DIBL), sub-threshold swing (SS), and threshold voltage (VT). These parameters are critical factors of the characteristics of tunnel field effect transistors. The Silvaco TCAD has been used to study the electrical characteristics of Si-NW TFET. Output (gate voltage-drain current) characteristics with channel dimensions were simulated. Results show that 50nm long nanowires with 9nm-18nm diameter and 3nm oxide thickness tend to have the best nanowire tunnel field effect transistor (Si-NW TFET) characteristics

Hypocone Reduction and Carabelli’s Traits in Contemporary Jordanians and the Association between Carabelli’s Trait and the Dimensions of the Maxillary First Permanent Molar

The objective of this study is to determine the prevalence of expression and bilateralism of two dental morphological traits in contemporary Jordanians: The hypocone reduction trait on the maxillary second permanent molar and Carabelli’s trait on maxillary permanent first and second molars. Furthermore, inter-trait correlation and the relationship of Carabelli’s traits with upper first molar dimensions were investigated. Three hundred subjects of school children at their 10th grade and of an average age of 15.5±0.4 years were involved. Alginate impressions for the maxillary arch were taken, dental casts were reproduced. The selected accurate casts were of 132 male- and 155 female-students. The frequencies of hypocone reduction trait on the maxillary second molar and Carabelli’s trait on the maxillary molars were examined. Buccolingual and mesiodistal diameters of the maxillary first molar were measured and recorded. Paired Sample t test and Nonparametric Correlation analysis were used for data analysis. Hypocone reduction trait on the maxillary second molar was found in 29.8 % of the examined students. Positive forms of Carabelli’s trait on first and second molars were observed in 65.0 % and 3.8 %, respectively. Nonparametric correlation analysis revealed positive association between Carabelli’s trait on first molar and hypocone reduction trait on the maxillary second molar. The presence of Carabelli’s trait on first molar was strongly associated with the increase of buccolingual, but not the mesiodistal, diameter. Bilateralism was found highly significant in the tested traits and both genders (p<0.001). This finding might be a sign of relatively low environmental stresses in the living Jordanian population and/or great ability of its individuals to buffer the adverse effects of such stresses

Exploring Media and Communication Students’ Perception of Egyptian Universities’ Use of Augmented Reality in Learning

The aim of this study was to investigate the perceptions of media students in Egypt universities about using augmented reality (AR) technology in learning. To achieve this, the study adopted Technology Acceptance Model (TAM) and utilized a survey questionnaire to collect data from students in seven universities across Egypt. The findings revealed that (i) the students had a positive perception about using AR in media and communication learning; (ii) many media students in Egypt were not fully aware of the various AR technology applications in media and communication education; (iii) the students identified several negative factors that may hinder their acceptance of AR technology as an instructional tool, such as poor connectivity, lack of free AR programs, and lack of training programs. Addressing these barriers could help promote the adoption of AR technology in media and communication learning among students in Egypt. The significance of the study lies in that it sheds light on the need for increased awareness and education of the potential benefits of using AR technology in media and communication learning