PC BASED SCADA SYSTEM FOR REVERSE OSMOSIS DESALINATION PLANTS

Reverse osmosis Desalination plants are a widely used application of water treatment engineering all over the world. Therefore devising control systems for these plants is a must in our modern automated industrial world as different systems has been devised yet their technology became obsolete by time. On the other hand complicated and over rated control systems are not convenient to use with these types of plants, especially for remote areas. Therefore, coming up with a monitoring system to monitor and control a Reverse Osmosis water treatment plant is a convenient solution with computer based software. The plant can be monitored using a PC enhanced with industrial automation software like LABVIEW® and a data acquisition card to build up a SCADA system for the water treatment plant. This work illustrates the structure and the installation of a flexible and low cost SCADA system. An ordinary PC with the appropriate interface and software operates the system. The system is installed to a lab scaled water plant which is designed and built. The system has proved the practicality of PC based SCADA systems over the conventional control systems

PC BASED SCADA SYSTEM FOR REVERSE OSMOSIS DESALINATION PLANTS

Reverse osmosis Desalination plants are a widely used application of water treatment engineering all over the world. Therefore devising control systems for these plants is a must in our modern automated industrial world as different systems has been devised yet their technology became obsolete by time. On the other hand complicated and over rated control systems are not convenient to use with these types of plants, especially for remote areas. Therefore, coming up with a monitoring system to monitor and control a Reverse Osmosis water treatment plant is a convenient solution with computer based software. The plant can be monitored using a PC enhanced with industrial automation software like LABVIEW® and a data acquisition card to build up a SCADA system for the water treatment plant. This work illustrates the structure and the installation of a flexible and low cost SCADA system. An ordinary PC with the appropriate interface and software operates the system. The system is installed to a lab scaled water plant which is designed and built. The system has proved the practicality of PC based SCADA systems over the conventional control systems

Use of production functions in assessing the profitability of shares of insurance companies

In this study the production functions (Cobb-Douglas, Zener-Rivanker, and the transcendental production function) have been used to assess the profitability of insurance companies, by reformulating these nonlinear functions based on the introduction of a set of variables that contribute to increase the explanatory capacity of the model. Then the best production function commensurate with the nature of the variable representing the profitability of insurance companies was chosen, to use it to assess the efficiency of their profitability versus the use of different factors of production and thus the possibility of using it in forecasting. It was found that the proposed model of the production function "Zener-Rivanker" is the best production functions representing the profitability of the Tawuniya and Bupa Insurance Companies. The proposed model of the Cobb-Douglas production function is suitable for the results of both Enaya and Sanad Cooperative Insurance Companies. The explanatory capacity of the production functions was also increased when the proposed variables were added (net subscribed premiums-net claims incurred)

Scheduling and securing drone charging system using particle swarm optimization and blockchain technology

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone's battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones' services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones' battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm's efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.Web of Science69art. no. 23

COVID-19 contact tracing and detection-based on blockchain technology

The fight against the COVID-19 pandemic still involves many struggles and challenges. The greatest challenge that most governments are currently facing is the lack of a precise, accurate, and automated mechanism for detecting and tracking new COVID-19 cases. In response to this challenge, this study proposes the first blockchain-based system, called the COVID-19 contact tracing system (CCTS), to verify, track, and detect new cases of COVID-19. The proposed system consists of four integrated components: an infection verifier subsystem, a mass surveillance subsystem, a P2P mobile application, and a blockchain platform for managing all transactions between the three subsystem models. To investigate the performance of the proposed system, CCTS has been simulated and tested against a created dataset consisting of 300 confirmed cases and 2539 contacts. Based on the metrics of the confusion matrix (i.e., recall, precision, accuracy, and F1 Score), the detection evaluation results proved that the proposed blockchain-based system achieved an average of accuracy of 75.79% and a false discovery rate (FDR) of 0.004 in recognizing persons in contact with COVID-19 patients within two different areas of infection covered by GPS. Moreover, the simulation results also demonstrated the success of the proposed system in performing self-estimation of infection probabilities and sending and receiving infection alerts in P2P communications in crowds of people by users. The infection probability results have been calculated using the binomial distribution function technique. This result can be considered unique compared with other similar systems in the literature. The new system could support governments, health authorities, and citizens in making critical decisions regarding infection detection, prediction, tracking, and avoiding the COVID-19 outbreak. Moreover, the functionality of the proposed CCTS can be adapted to work against any other similar pandemics in the future.Web of Science84art. no. 7

Early and delayed suture adjustments after adjustable suture strabismus surgery: a randomized controlled trial

Background: Adjustable sutures increase the success rate of strabismus surgery. However, the optimal timing of postoperative suture adjustment remains controversial. This trial was aimed at comparing the surgical outcomes and pain scores of early or 2 – 4 h and delayed or 24 h postoperative suture adjustment in adult patients undergoing strabismus surgery. Methods: An open-label, prospective, randomized, comparative interventional study was performed in consecutive adult patients scheduled for eye muscle surgery. Patients were randomized into two groups: the early group, with suture adjustment 2 – 4 h postoperatively, and the delayed group, with suture adjustment 24 h postoperatively. Subjective pain scores during the adjustment were also analyzed. The angles of misalignment at 1 and 3 months and the success rate at 3 months postoperatively were compared. Results: Forty-five (90%) patients completed the follow-up, including 23 (92%) in the early adjustment group and 22 (88%) in the delayed adjustment group, with a mean (standard deviation) age of 25.6 (9.5) years and a male-to-female ratio of 46.7:53.3. Thirty patients (66.7%) had exotropia, and 15 (33.3%) patients had esotropia. Both groups had comparable baseline characteristics (all P > 0.05). The mean pain scores during adjustment did not differ significantly between groups (P > 0.05). The postoperative angles of alignment were comparable between the groups before suture adjustment and at the 1- and 3-month follow-ups (all P > 0.05). The success rate in the early adjustment group was slightly higher (87.0% versus 63.6%), but the difference was not statistically significant (P > 0.05). The success rate was comparable between the groups in patients with esotropia or exotropia (both P > 0.05). Conclusions: Although the early adjustment group had a slightly higher success rate, the difference was not significant. Both groups had comparable subjective pain scores during adjustment. Future clinical trials should be performed different time intervals for postoperative suture adjustment, and subjective and objective outcomes, such as diplopia and stereopsis, should be compared between patients with a first strabismus surgery and those who underwent reoperation. This could better resolve the persistent controversy related to the optimal time for suture adjustment

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images

Transbronchial and transesophageal fine-needle aspiration using a single ultrasound bronchoscope in the diagnosis of locoregional recurrence of surgically-treated lung cancer

The present study sought to evaluate the usefulness of EBUS-TBNA in the diagnosis of locoregional recurrence of lung cancer in a cohort of lung cancer patients who were previously treated surgically, and describe our initial experience of EUS-B-FNA in this clinical scenario. We retrospectively studied the clinical records of all patients with a previous surgically-treated lung cancer who were referred to our bronchoscopy unit after suspicion of locoregional recurrence. The diagnostic sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and overall accuracy of EBUS-TBNA for the diagnosis of locoregional recurrence were evaluated. Seventy-three patients were included. EBUS-TBNA confirmed malignancy in 40 patients: 34 confirmed to have locoregional recurrence, six had metachronous tumours. Of the 33 patients with non-malignant EBUS-TBNA; 2 had specific non-malignant diseases, 26 underwent radiological follow up and 5 patients underwent surgery. Of the 26 patients who had radiological follow up; 18 remained stable, three presented thoracic radiological progression and 5 presented extrathoracic progression. Of the 5 patients who underwent surgery; 3 had metachronous tumours, one confirmed to be a true negative and one presented nodal invasion. Seven patients underwent EUS-B-FNA, four of them confirmed to have recurrence. The sensitivity, specificity, NPV, PPV and overall accuracy of EBUS-TBNA for the diagnosis of locoregional recurrence were 80.9, 100, 69.2, 100 and 86.6% respectively. EBUS-TBNA is an accurate procedure for the diagnosis of locoregional recurrence of surgically-treated lung cancer. EUS-B-FNA combined with EBUS-TBNA broads the diagnostic yield of EBUS-TBNA alone

Metaverse-IDS: Deep learning-based intrusion detection system for Metaverse-IoT networks

Combining the metaverse and the Internet of Things (IoT) will lead to the development of diverse, virtual, and more advanced networks in the future. The integration of IoT networks with the metaverse will enable more meaningful connections between the 'real' and 'virtual' worlds, allowing for real-time data analysis, access, and processing. However, these metaverse-IoT networks will face numerous security and privacy threats. Intrusion Detection Systems (IDS) offer an effective means of early detection for such attacks. Nevertheless, the metaverse generates substantial volumes of data due to its interactive nature and the multitude of user interactions within virtual environments, posing a computational challenge for building an intrusion detection system. To address this challenge, this paper introduces an innovative intrusion detection system model based on deep learning. This model aims to detect most attacks targeting metaverse-IoT communications and combines two techniques: KPCA (Kernel Principal Component Analysis which was used for attack feature extraction and CNN (Convolutional Neural Networks for attack recognition and classification. The efficiency of this proposed IDS model is assessed using two widely recognized benchmark datasets, BoT-IoT and ToN-IoT, which contain various IoT attacks potentially targeting IoT communications. Experimental results confirmed the effectiveness of the proposed IDS model in identifying 12 classes of attacks relevant to metaverse-IoT, achieving a remarkable accuracy of and a False Negative Rate FNR less than . Furthermore, when compared with other models in the literature, our IDS model demonstrates superior performance in attack detection accuracy