Pulse Coded Neural Network Implementation In VLSI

A neural network that encodes signals in terms of pulses has been designed and fabricated. The neural network components are described in detail. As a test case, a two-layer network is implemented. A preliminary test result shows some promise and some limitations of the desig

Compliance of hand written transfusion requisition form and improvement after online request - A clinical audit

Objective: To assess the compliance of healthcare personnel with regard to sending completely filled transfusion requisition forms.Methods: The audit was conducted at Aga Khan University Hospital, Karachi, and comprised requisition slips received at the hospital blood bank from September 2014 to February 2015. The British Committee for Standards in Haematology guidelines was used as the standard. Percentage of each variable on the proforma was analsyed. Rating \u3c50% for each form was defined as needs improvement , 51-99% as good compliance and 100% as excellent compliance . After implementing strategies to increase awareness and the launching of an online transfusion requisition form, a re-audit of physician compliance was done from February to April 2016 and the results were compared with the initial audit.. Data was analysed using SPSS 21.Results: The audit and the re-audit both comprised 1000 transfusion requisition forms each. In the audit, The sum of total scores of all the transfusion requisition forms was 4911, indicating a compliance rate of 46.9%, while the corresponding numbers in the re-audit were 10000 and 100%.Conclusions: The implementation of online blood transfusion requisition system had a positive impact on compliance rate

An unusual case of left ventricular aneurysm in duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) leads to structural heart disease, including dilated cardiomyopathy, in 90% of patients >18 years of age. Despite the ubiquity of cardiomyopathy associated with DMD, ventricular aneurysms in these patients have rarely been reported. We present a case of a basal inferoposterior aneurysm of the left ventricle in a 23-year-old male patient with DMD

Stochastic Computing Correlation Utilization in Convolutional Neural Network Basic Functions

In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work

FPGA-based real-time moving target detection system for unmanned aerial vehicle application

Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird's eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels' resolution greyscale videos

Therapeutic Targets and Signaling Pathways for Diagnosis of Myeloma

Multiple myeloma (MM) is a malignancy of plasma cells that not only shows different clinical behavior but also depicts heterogeneous groups at molecular level. The prognosis of the disease has been dramatically changed with the arrival of new drugs in the past few years. In this context of better therapeutic agents, there are important challenges for accurate evaluation of patients by better prognostic and predictive tools. Transcriptomic studies have largely added to decipher MM heterogeneity, dividing MM patients into different subgroups according to prognosis. Micro-arrays and more recently RNA sequencing have helped in evaluating coding and non-coding genes, mutations, unique transcriptome convertors and different splicing events giving new information concerning biology, outcome and treatment options. Initial data from gene expression profiling studies have also pointed out genes that predict prognosis, i.e., CSK1-B, and can deliver pharmacogenomics and biologic vision into the pathophysiology, targeted treatment, and future direction. Importantly, we suggest that all prospective studies and clinical trials now accept genetic testing and risk stratification of MM patients. In this review, we discuss the part and effect of gene expression profiling in myeloma

IoT Threat Detection Testbed Using Generative Adversarial Networks

The Internet of Things(IoT) paradigm provides persistent sensing and data collection capabilities and is becoming increasingly prevalent across many market sectors. However, most IoT devices emphasize usability and function over security, making them very vulnerable to malicious exploits. This concern is evidenced by the increased use of compromised IoT devices in large scale bot networks (botnets) to launch distributed denial of service(DDoS) attacks against high value targets. Unsecured IoT systems can also provide entry points to private networks, allowing adversaries relatively easy access to valuable resources and services. Indeed, these evolving IoT threat vectors (ranging from brute force attacks to remote code execution exploits) are posing key challenges. Moreover, many traditional security mechanisms are not amenable for deployment on smaller resource-constrained IoT platforms. As a result, researchers have been developing a range of methods for IoT security, with many strategies using advanced machine learning(ML) techniques. Along these lines, this paper presents a novel generative adversarial network(GAN) solution to detect threats from malicious IoT devices both inside and outside a network. This model is trained using both benign IoT traffic and global darknet data and further evaluated in a testbed with real IoT devices and malware threats.Comment: 8 pages, 5 figure

THE PERCEPTION AND EXPERIENCE OF NURSING STUDENTS REGARDING THE USE OF ACTIVE LEARNING STRATEGIES

Abstract Active learning is a way of teaching rather than a particular, specialized technique. It necessitates active student participation in carefully planned teacher-structured classroom activities. Objective: This study aims to explore nursing students' perceptions and experiences regarding using active learning strategies. Methodology: This cross-sectional study was conducted at the private nursing institute in Karachi, Pakistan, from May to August 2023. Moreover, this study used a convenient sampling technique. The eighty-seven student participants are included in this study. Results: The survey results reveal that a substantial proportion of nursing students strongly agree with the effectiveness and benefits of active learning strategies. Specifically, 40.2% strongly agree that active learning enhances their understanding of complex nursing concepts, while 47.1% and 43.7% strongly agree that active learning activities are more engaging than traditional lectures. Furthermore, 55.2% strongly agree that active learning helps them retain and apply knowledge effectively, and 51.7% strongly agree that it encourages teamwork and collaboration among nursing students. Notably, 83.9% of students prefer active learning over traditional lecture-based teaching. Additionally, 35.6% strongly believe that nursing education can enhance the implementation of active learning strategies through learning objectives. Conclusion: The survey overwhelmingly supports active learning strategies among nursing students. Most strongly agree that active learning enhances understanding, engagement, and knowledge retention, favoring it over traditional lectures, mainly through case studies. Many also believe that integrating clear learning objectives can enhance its effectiveness. These findings underscore the widespread acknowledgment of active learning's positive impact on nursing education, emphasizing its role in improving comprehension and student engagement

Antimicrobial activities of gynura procumbens leaves extract against selected bacteria / Liliwirianis Nawi ...[et al.]

Gynura procumbens or Sambung Nyawa is a tropical plants species from the Asteraceae family. The objectives of this study were to determine antimicrobial activity and Minimal Inhibition Concentration (MIC) of G. procumbens leaf extracts against selective Gram-positive and Gramnegative bacteria. Methanol and hexane were the two extraction solvents that had been used. Four concentrations of extracts; 50 mg/mL, 100 mg/mL, 200 mg/mL, and 400 mg/mL were prepared for antimicrobial activity. Meanwhile for MIC determination, 6.25 mg/mL, 12.5 mg/mL, 25 mg/mL and 50 mg/mL concentrations were prepared. By using disc diffusion method, the methanol extract of G. procumbens leaves showed antimicrobial activities against Staphylococcus aureus, Bacillus subtilis, Klebsiella pneumoniae, and Pseudomonas aeruginosa compared to hexane extract. Highest antimicrobial activities were recorded against S. aureus at 400 mg/mL concentrations with 10.5 mm of inhibition zone. Broth dilution assay resulted MIC for methanol crude extract against S. aureus, B. subtilis, K. pneumoniae, and P. aeruginosa were at 12.5 mg/mL, 25 mg/mL, and 50 mg/mL, respectivel