Hydrostatic Penetration Testing of Protective Glove Materials Using Water and Synthetic Blood to Evaluate Hole Size and Screen Mesh Using an Automated Pressure Delivery System.

There are several international standards that address the resistance of chemical protective clothing materials to the penetration by liquids. The hydrostatic pressure has been documented to discriminate between protective clothing material performance and correlates with visual penetration results that are obtained with human factors validation. The same methodology, based on hydrostatic pressure equipment, is referenced also in other standards addressing penetration resistance of protective clothing and glove materials against synthetic blood or blood-borne pathogens. In this study, we present an automated hydrostatic penetration testing that integrates testing procedures from several standards to evaluate the resistance of materials to penetration by liquids under pressure. The automated control system allows the user to select a specific test method and automatically sets a stepped pressurization protocol to test the material. A pass or a fail result is produced at a certain time and pressure. As an example of application, the penetration of synthetic blood was assessed through gloves made from different materials with ISO 16603, method B, one of the five possible penetration methods and protocols available in the test equipment. The results indicate that the developed system facilitates the application of test methods used to evaluate the barrier effectiveness against liquids of materials used for protective clothing and gloves and show up that the characteristics of the retention grid used have a decisive influence on the test results. In some of the tested glove materials, holes were intentionally performed with needles with different gauges. The capacity of pinhole detection in gloves was evaluated according to the test method selected and compared with results obtained with the classic water leak test method for gloves described in EN ISO 374-2.

Traditional uses of Cannabis: An analysis of the CANNUSE database.

ETHNOPHARMACOLOGICAL RELEVANCE: Cannabis is one of the most versatile genera in terms of plant use and has been exploited by humans for millennia. Nowadays, Cannabis is the centre of many scientific studies, most of them focusing on chemical composition and medicinal values. While new and varied applications are continuously being developed, the knowledge surrounding less common uses of the plant is slowly disappearing. AIM OF THE REVIEW: We have analysed diversity of global data of Cannabis traditional uses, to investigate if certain plant parts are significantly associated with particular Cannabis use. We wanted to uncover potential associations between the plant parts used for the treatment of different body systems and ailments. MATERIALS AND METHODS: We have analysed the extensive database of Cannabis traditional uses (CANNUSE). This database contains 2330 data entries of Cannabis ethnobotanical uses from over 40 countries across the world. The dataset was divided into five general groups based on the type of use: medicinal, alimentary, psychoactive, fibre and other uses. Given the abundance of human medicinal uses, detailed analysis was done on the subset of 1167 data entries. We analysed the relationship between 16 body system categories and ailments treated with Cannabis plant parts. We used a Pearson's chi-square and Fisher's exact test, to determine which Cannabis parts are characteristic of treatment for specific ailments. RESULTS: In this dataset, the majority of reports were represented by medicinal (75.41%), followed by psychoactive (8.35%) and alimentary (7.29%) use. The most commonly used plant parts were leaf (50.51%), seed (15.38%) and inflorescence (11.35%). We found that different Cannabis plant parts were significantly associated with different uses; the leaf was typically used for medicinal, seed for alimentary and inflorescence for psychoactive use. Regarding the human medicinal uses, most common were reports for treatments of the digestive system and nutritional disorders (17.66%), nervous system and mental disorders (16.24%), followed by pain and inflammations (12.21%). We found a significant relationship between the use of certain Cannabis parts and treatment of ailments and body systems categories; leaf was significantly associated with treatment of two categories: skin and subcutaneous tissue disorders and circulatory system and blood disorders; seed use was associated with musculoskeletal system disorders and traumas; while inflorescence use shows a statistical support for treatment of nervous system and mental disorders. CONCLUSION: Several pharmaceutical companies are intensely working on developing new drugs with isolated chemical compounds or crude extracts, almost exclusively from Cannabis inflorescences. However, our review revealed that use of leaf or seed in traditional medicine is often more important than use of inflorescence for the treatment of certain ailments. A review of traditional medicine provides a body of knowledge and an initial pathway to identify landraces and plant parts that could have an important role in future medicinal research. We are confident that traditional medicine still has a large potential for modern medicine. As more information on Cannabis diversity (genetics, biochemistry, and clinical studies) becomes available, ethnobotanical data are poised to be of much greater significance.

Simulation Tool for the Analysis of Cooperative Localization Algorithms for Wireless Sensor Networks.

Within the context of the Internet of Things (IoT) and the Location of Things (LoT) service, this paper presents an interactive tool to quantitatively analyze the performance of cooperative localization techniques for wireless sensor networks (WSNs). In these types of algorithms, nodes help each other determine their location based on some signal metrics such as time of arrival (TOA), received signal strength (RSS), or a fusion of them. The developed tool is intended to provide researchers and designers a fast way to measure the performance of localization algorithms considering specific network topologies. Using TOA or RSS models, the Crámer-Rao lower bound (CRLB) has been implemented within the tool. This lower bound can be used as a benchmark for testing a particular algorithm for specific channel characteristics and WSN topology, which allows determination if the necessary accuracy for a specific application is possible. Furthermore, the tool allows us to consider independent characteristics for each node in the WSN. This feature allows the avoidance of the typical "disk graph model," which is usually applied to test cooperative localization algorithms. The tool allows us to run Monte-Carlo simulations and generate statistical reports. A set of basic illustrative examples are described comparing the performance of different localization algorithms and showing the capabilities of the presented tool.