Middle East Respiratory Syndrome (MERS) and Novel Coronavirus Disease-2019 (COVID-19): From causes to preventions in Saudi Arabia

Saudi Arabia is one of the countries that has been affected by COVID-19. At the beginning of March 2020, it revealed a steadily rising number of laboratory-confirmed cases. By 20th May 2020, 59,854 infected cases had been confirmed, with 329 deaths. To prevent a further outbreak of COVID-19, this article discusses the current understanding of COVID-19 and compares it with the outbreak of Middle East Respiratory Syndrome (MERS) in 2012 in Saudi Arabia. It also discusses the causes, transmission, symptoms, diagnosis, treatments and prevention measures to identify an applicable measure to control COVID-19

Microcontroller-based transient signal analysis and distributed system for intelligent process monitoring

The research presented in this thesis considers the feasibility of utilising dsPICs (digital signal controllers) in the development of effective monitoring systems which have the capability to adapt to changes in operating conditions and can be quickly calibrated to suit a range of applications, thus helping to reduce the development time constraint. The capability of these monitoring solutions to detect and isolate faults occurring in pneumatic processes is investigated and their effectiveness verified. Three applications are considered gas pipe leakage, linear actuator operations and gripper action. In each case, solutions are developed based upon the dsPIC. The solutions utilise the analysis of pressure transients to overcome the limitation in the dsPIC memory. The deployment of minimal sensors and electronics was essential to optimise the cost of the system. Leak detection techniques are developed with application to gas fitting pipes. The speed at which correct decisions are determined was the essence of this work. The solutions are tested, compared and their capability validated using pipes which had been rejected according to industrial standards. In this application a dsPIC digital signal controller and a pressure sensor were deployed, thus ensuring a low cost monitoring solution. Linear actuator "end of stroke" monitoring has, previously, largely been possible using limit switches. A more challenging method based upon the deployment of a pressure sensor is outlined. Monitoring model surfaces were obtained and their capability to determine the health of the process was proved, at various supply pressures. With regard to the gripper monitoring, a performance surface by which the gripper action can be monitored is generated and embedded within the dsPIC. Various faults are simulated and their effect on the gripper performance investigated. Leakage and blockage are also investigated at various places in the pneumatic circuit to allow for an algorithm to be devised. Faults may be detected and isolated, and their locations identified to allow for timely recovery treatment, thus supporting an enhanced process monitoring strategy.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Tera Multi Terrain Mobility Aid Chassis

The natural environment poses a significant number of obstacles and dynamic settings that makes mobility difficult for those with physical and mobility impairments. To approach this problem, a suspension was designed using inspiration from the early Mars rovers developed by NASA for traversing the varied Martian landscape. The course of the project followed the direction of a start-up through problem identification, early design generation and review, and final design production. The project outcome, through client request and proven market research, aimed to produce a multi-terrain wheelchair. The final product is a kinematic body with mobile front “legs” and a rotational degree of freedom between the two supporting halves, allowing for uneven terrain changes between the two sides and for overcoming step height obstacles. A linkage suspension system was designed to create mobility in the basic design and another suspension piece was created in order to provide payload or patient stability on the product. The final project outcome delivered a 3D modeled package of components and assemblies as well as basic material strength analysis to verify design strength and support qualifications before physical assembly

Microcontroller-based transient signal analysis and distributed system for intelligent process monitoring

The research presented in this thesis considers the feasibility of utilising dsPICs (digital signal controllers) in the development of effective monitoring systems which have the capability to adapt to changes in operating conditions and can be quickly calibrated to suit a range of applications, thus helping to reduce the development time constraint. The capability of these monitoring solutions to detect and isolate faults occurring in pneumatic processes is investigated and their effectiveness verified. Three applications are considered gas pipe leakage, linear actuator operations and gripper action. In each case, solutions are developed based upon the dsPIC. The solutions utilise the analysis of pressure transients to overcome the limitation in the dsPIC memory. The deployment of minimal sensors and electronics was essential to optimise the cost of the system. Leak detection techniques are developed with application to gas fitting pipes. The speed at which correct decisions are determined was the essence of this work. The solutions are tested, compared and their capability validated using pipes which had been rejected according to industrial standards. In this application a dsPIC digital signal controller and a pressure sensor were deployed, thus ensuring a low cost monitoring solution. Linear actuator 'end of stroke' monitoring has, previously, largely been possible using limit switches. A more challenging method based upon the deployment of a pressure sensor is outlined. Monitoring model surfaces were obtained and their capability to determine the health of the process was proved, at various supply pressures. With regard to the gripper monitoring, a performance surface by which the gripper action can be monitored is generated and embedded within the dsPIC. Various faults are simulated and their effect on the gripper performance investigated. Leakage and blockage are also investigated at various places in the pneumatic circuit to allow for an algorithm to be devised. Faults may be detected and isolated, and their locations identified to allow for timely recovery treatment, thus supporting an enhanced process monitoring strategy

Characterization and evaluation of the performance of starch and cellulose as excipients for direct compression technique

Purpose: To investigate the influence of two often-used excipients (starch and microcrystalline cellulose) on the physical properties of powder blends and tablets that contain mannitol as diluent.Methods: Powder and powder mixtures of three commonly used excipients (starch, mannitol and microcrystalline cellulose) were thoroughly examined using the angle of repose for flowability, particle size analyzer to determine the diameter of the particles, scanning electron microscopy (SEM) for morphological assessment, and x-ray diffraction to determine crystalline/amorphous characteristics. Tablets were prepared by direct compression technique and were evaluated for mechanical strength and disintegration behavior as part of quality control test.Results: The results showed that increase in MCC concentration of the mixture leads to significantly enhanced flowability (p < 0.05) when compared to starch. The angle of repose for mannitol/MCC powder mixture with 70 % w/w MCC was approximately 29°, indicating good flow properties of thepowder mix. Moreover, starch tablets containing MCC exhibited better mechanical strength and longer disintegration time, while, at 1:1 ratio of MCC and mannitol, tablet disintegration was faster (33.0 ± 5.2s)Conclusion: MCC (at 30 %w/w in the blend) produces optimal flow of the powder blend and superior mechanical strength, Keywords: Tablet disintegration, Flowability, Starch, Hardness, Mechanical strengt

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity , using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset ( H-Activity ), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.publishedVersio

Assessment of Treatment Response after Pressurized Intra-Peritoneal Aerosol Chemotherapy (PIPAC) for Appendiceal Peritoneal Metastases

Background The aim of this study was to analyse survival and surrogates for oncological response after PIPAC for appendiceal tumours. Methods This retrospective cohort study included consecutive patients with appendiceal peritoneal metastases (PM) treated in experienced PIPAC centers. Primary outcome measure was overall survival (OS) from the date of diagnosis of PM and from the start of PIPAC. Predefined secondary outcome included radiological response (RECIST criteria), repeat laparoscopy and peritoneal cancer index (PCI), histological response assessed by the Peritoneal regression grading system (PRGS) and clinical response. Results Final analysis included 77 consecutive patients (208 PIPAC procedures) from 15 centres. Median OS was 30 months (23.00–46.00) from time of diagnosis and 19 months (13.00–28.00) from start of PIPAC. 35/77 patients (45%) had ≥3 procedures (pp: per protocol). Objective response at PIPAC3 was as follows: RECIST: complete response 4 (11.4%), 11 (31.4%) partial/stable; mean PRGS at PIPAC3: 1.8 ± 0.9. Median PCI: 21 (IQR 18–27) vs. 22 (IQR 17–28) at baseline (p = 0.59); 21 (60%) and 18 (51%) patients were symptomatic at baseline and PIPAC3, respectively (p = 0.873). Median OS in the pp cohort was 22.00 months (19.00–NA) from 1st PIPAC. Conclusion Patients with PM of appendiceal origin had objective treatment response after PIPAC and encouraging survival curves call for further prospective evaluation

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis