KNOWLEDGE OF MICROBIAL CONTAMINATION OF SPHYGMOMANOMETERS IN HEALTHCARE FACILITIES IN BENIN CITY

Nosocomial infections are infections that patients acquire while receiving treatment for other health conditions within a healthcare setting or facility. This study aims to determine the level of awareness of healthcare providers on the role sphygmomanometers play in the spread of nosocomial infections and to isolate microorganisms in sphygmomanometer cuffs used in healthcare facilities. A structured, self-administered questionnaire was designed and administered to healthcare practitioners of two tertiary hospitals and community pharmacies in Benin City. Microbial contamination of sphygmomanometer cuffs was investigated following the standard isolation and identification techniques for microorganisms. A total of 217 responded; 27.2% pharmacists, 33.2% doctors and 39.6% nurses. The majority (50.2%) were between the ages of 20 – 30 years. 65.4% were females and 51.6% were single. Ninety-four percent (94%) of the total respondents said that microorganisms are present in the inner cuffs of sphygmomanometers, 76% said microorganisms on the cuffs are sources of nosocomial infections while 80.6% said patients can be infected with the use of sphygmomanometers. A total of 192 swabbed samples were collected from 64 cuffs in the healthcare facilities, 46.5% were bacteria and 53.5% fungi. The most isolated organisms were candida species 42(21%), Staphylococcus aureus 41(20.5% of which 28(68.3%) were methicillin resistant.), Mucor 34(17%), Aspergillus species 23(11.5%). Ninety four percent (94%) of respondents had good knowledge that blood pressure cuffs play a role in the spread of nosocomial infections. The sphygmomanometer cuffs were contaminated with pathogenic microorganisms implicated in nosocomial infections.

FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales

Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available.

Comparing system identification techniques for identifying human-like walking controllers

While human walking has been well studied, the exact controller is unknown. This paper used human experimental walking data and system identification techniques to infer a human-like controller for a spring-loaded inverted pendulum (SLIP) model. Because the best system identification technique is unknown, three methods were used and compared. First, a linear system was found using ordinary least squares. A second linear system was found that both encoded the linearized SLIP model and matched the first linear system as closely as possible. A third nonlinear system used sparse identification of nonlinear dynamics (SINDY). When directly mapping states from the start to the end of a step, all three methods were accurate, with errors below 10% of the mean experimental values in most cases. When using the controllers in simulation, the errors were significantly higher but remained below 10% for all but one state. Thus, all three system identification methods generated accurate system models. Somewhat surprisingly, the linearized system was the most accurate, followed closely by SINDY. This suggests that nonlinear system identification techniques are not needed when finding a discrete human gait controller, at least for unperturbed walking. It may also suggest that human control of normal, unperturbed walking is approximately linear.

How Filipino Fishers Use Traditional Knowledge in Identifying Species of Juvenile Mangrove Crabs for Grow-out Culture

Mangrove crab growers in the Philippines still rely on wild-caught late instar to early juvenile mangrove crablets, as supplies from hatcheries are limited. Any batch of crablets caught from the wild is a mix of the three native species under the genus Scylla. Scylla species have different growth rates. Since grow-out culture depends heavily on species' growth, growers should be able to distinguish the species as early as the juvenile stage, which is taxonomically difficult. This study was done to consolidate low-cost traditional identification techniques for juvenile Scylla from fishers of the Philippines for future validation. Focused group discussions were done in fishing communities from Bataan, Pangasinan, and Cagayan on the island of Luzon. The study was continued through online surveys, as travel was restricted due to the Covid-19 pandemic. Results indicate that 70.58% of respondents identify the species of crabs by looking at their claws and 55.88% observe the color of the crabs. Almost half, or 41.17% of respondents, consider the width and size of the carapace. Unique methods in certain Philippine regions include observation of the behavior patterns, carapace texture, rate of weight gain, and seasonality. Validation of the traditional practices identified in this study would result in a reliable "at-a-glance" method of identifying juvenile Scylla in the Philippines, which would shorten the culture period, improve production gains, and manage local populations.

Contemporary Physical Clone-Resistant Identity for IoTs and Emerging Technologies

Internet of things (IoT) technologies have recently gained much interest from numerous industries, where devices, machines, sensors, or simply things are linked with each other over open communication networks. However, such an operation environment brings new security threats and technology challenges in securing and stabilizing such large systems in the IoT world. Device identity in such an environment is an essential security requirement as a secure anchor for most applications towards clone-resistant resilient operational security. This paper analyzes different contemporary authenticated identification techniques and discusses possible future technologies for physically clone-resistant IoT units. Two categories of identification techniques to counteract cloning IoT units are discussed. The first category is inherently cloneable and includes the classical identification mechanisms based on secret and public key cryptography. Such techniques deploy mainly secret keys stored permanently somewhere in the IoT devices as classical means to make units clone-resistant. However, such techniques are inherently cloneable as the manufacturer or device personalizers can clone them by re-using the same secret key (which must be known to somebody) or reveal keys to third parties to create cloned entities. In contrast, the second, more resilient category is inherently unclonable because it deploys unknown and hard to predict born analog modules such as physical unclonable functions (PUFs) or mutated digital modules and so-called secret unknown ciphers (SUCs). Both techniques are DNA-like identities and hard to predict and clone even by the manufacturer itself. Born PUFs were introduced two decades ago; however, PUFs as analog functions failed to serve as practically usable unclonable electronic identities due to being costly, unstable/inconsistent, and non-practical for mass application. To overcome the drawbacks of analog PUFs, SUCs techniques were introduced a decade ago. SUCs, as mutated modules, are highly consistent, being digital modules. However, as self-mutated digital modules, they offer only clone-resistant identities. Therefore, the SUC technique is proposed as a promising clone-resistant technology embedded in emerging IoT units in non-volatile self-reconfiguring devices. The main threats and expected security requirements in the emerging IoT applications are postulated. Finally, the presented techniques are analyzed, classified, and compared considering security, performance, and complexity given future expected IoT security features and requirements.