Probabilistic machine learning approaches has been successfully applied in various applications and is gaining more and more popularity. But the success of such approaches are based on the quality of ...the data. Getting quality data is the biggest challenge for most of the real-life applications and our application domain, i.e. industrial cleaning process, is no exception. In our application domain, the data collection is mostly performed manually without using any standards and is highly influenced by the expertise and interpretation of individual cleaning personnel. We have developed a Bayesain predictive assistance system (BPAS) that uses a real-life cleaning data to provide decision support to the cleaning personnel. In this paper, we extend our BPAS and propose a hybrid approach to develop an assistance system for resource optimization in industrial cleaning processes. The proposed approach, which combines Bayesian network and rule-based system, aims at increasing the robustness and the stability of the assistance system.
Optimizing the resource consumption by the products (machines) and making them environment friendly is the aim of almost all producers today. May it be due to cost of resources, their limited ...availability, their affect on the environment or consumer awareness. Ample research is being carried out at national and international level for resource optimization. Adding intelligence and learning capability is being increasingly used as an approach for resource optimization. Different methods and models for machine learning are available in the literature. Bayesian network is one of the widely used learning model for resource optimization in wide range of applications 1, 2. In this paper, we present the use of Bayesian network for resource optimization and decision support system in an industrial cleaning process. The proposed Bayesian predictive assistance system assists the cleaner in choosing the optimal parameters and would be a self-learning system that stores the successful cleaning results in a global database for future cleaning cycle.
Bayesian networks (BNs) have been used in different contexts of decision support solutions such as directive, strategic, tactical and operational. These contexts differ from each other only in the ...realization of the decision support in terms of time. The real-time implementation of BN in an embedded system for resource optimization is very challenging because of the low computation capacity in embedded systems and, to the best of our knowledge, has not been reported yet. In this paper, we present a BN based predictive assistance system that uses real-life data to perform the real-time decision support in industrial cleaning processes.
Because energy efficiency is gaining more importance these days and Bluetooth Low Energy (BLE) could be used to make use of potential everyday objects into Internet of Things (IOT) - a software, ...platform and vendor independent common service interface that can be used in such low resource devices has high potential. OPC UA is an emerging middleware solution that addresses the above points but is bulky due to its abundant features. Further optimization is necessary to bring the OPC UA into such resource-limited devices. We have scaled down the OPC UA protocol stack footprint down to the chip level 16. In this paper, we propose an optimization approach to minimize the OPC UA network footprint.
Industrial Automation (IA) applications require deterministic communication channels to ensure a reliable operation. As the wireless medium is a shared medium used by many other wireless ...technologies, a deterministic medium access method (MAM) is necessary. An improvement of the coexistence behavior can be achieved by applying adaptive MAMs, but they cannot meet any real-time demands. A promising approach to meet real-time as well as coexistence demands are cognitive MAMs. We evaluate the performance of three different cognitive MAMs which differ in the probabilistic prediction model: Two methods are based on Markov modelling (MM) and one method is based on an auto-regressive (AR) model. The MAMs are experimentally evaluated in a worst case wireless measurement scenario.
The increasing power demands and growing awareness for sustainable and green energy has led to distributed generation of power from different sources. This transition from centralized to ...a distributed power generation has increased the necessity to upgrade the traditional grid. The future grid, i.e. Smart Grid, should offer two way flow of power and information. Smart grid needs to intelligently manage the power generation, transmission, and distribution to generate optimal power resources and adapt consumers to those power resources. In addition, it should support smart metering and monitoring to reduce energy consumption and cost. This intelligent management demand near real time communication between the power generators, consumer utilities and the control center. Thus machine-to-machine (M2M) communication is the necessity of future smart grid applications.
Smart grid is a huge infrastructure and its components are located at far-off locations. Hence, wired and short range wireless communication solutions would not be ideal for smart grid applications. This paper presents the performance evaluation of different cellular communication systems as a solution for M2M communication in smart grid applications.
Typically, cognitive radio systems either sense the channel just before transmission or perform this task periodically in order to remain aware about the operational environment. However, a channel ...sensed as `free' can become busy during the transmission of the cognitive system resulting in harmful collisions and unnecessary interruptions in the secondary user data transmission. As a solution, predictive based approaches has been proposed and has shown promising results in simulated environments. However, modeling real-time, dynamic, coexisting environments demand investigation with real-time demonstrators. This paper investigates industrial coexisting environments and illustrates the prediction model selection and its parameter estimation criteria. Based on the investigation a real-time testbed is implemented using a CC2500 TRX and MSP430 μC based platform.
Invasive bacterial disease (IBD; including pneumonia, meningitis, sepsis) is a major cause of morbidity and mortality in children in low-income countries.
We analyzed data from a surveillance study ...of suspected community-acquired IBD in children <15 years of age in Kathmandu, Nepal, from 2005 to 2013 before introduction of pneumococcal conjugate vaccines (PCV). We detailed the serotype-specific distribution of invasive pneumococcal disease (IPD) and incorporated antigen and PCR testing of cerebrospinal fluid (CSF) from children with meningitis.
Enhanced surveillance of IBD was undertaken during 2005-2006 and 2010-2013. During enhanced surveillance, a total of 7956 children were recruited of whom 7754 had blood or CSF culture results available for analysis, and 342 (4%) had a pathogen isolated. From 2007 to 2009, all 376 positive culture results were available, with 259 pathogens isolated (and 117 contaminants). Salmonella enterica serovar Typhi was the most prevalent pathogen isolated (167 cases, 28% of pathogens), followed by Streptococcus pneumoniae (98 cases, 16% pathogens). Approximately, 73% and 78% of pneumococcal serotypes were contained in 10-valent and 13-valent PCV, respectively. Most cases of invasive pneumococcal disease (IPD) were among children ≥5 years of age from 2008 onward. Antigen and PCR testing of CSF for pneumococci, Haemophilus influenzae type b and meningococci increased the number of these pathogens identified from 33 (culture) to 68 (culture/antigen/PCR testing).
S. enterica serovar Typhi and S. pneumoniae accounted for 44% of pathogens isolated. Most pneumococcal isolates were of serotypes contained in PCVs. Antigen and PCR testing of CSF improves sensitivity for IBD pathogens.
Total thirty-six dihydroxylated 2,6-diphenyl-4-aryl pyridines were designed, synthesized and tested for topo I and II inhibitory activity and cytotoxicity for the development of novel anticancer ...agents. Display omitted
A new series of thirty-six dihydroxylated 2,6-diphenyl-4-aryl pyridines containing hydroxyl groups at the ortho, meta, or para position of 2- and 6-phenyl rings attached to the central pyridine were designed and synthesized. They were evaluated for topoisomerase I and II inhibitory activity and cytotoxicity against several human cancer cell lines for the development of novel anticancer agents. Most of the compounds with hydroxyl moiety either at the meta or para position of 2- or 6-phenyl ring in combination with thienyl or furyl group at 4-position of central pyridine displayed significant topoisomerase II inhibitory activity and cytotoxicity. Positive correlation between topoisomerase II inhibitory activity and cytotoxicity was observed for the compounds 9–11, 15–17, 19, 21–23, 28, and 41. Among all the synthesized compounds, compound 17 emerged as the most promising topoisomerase II inhibitor with significant cytotoxicity.
Background: Pharmacovigilance is the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs) and any other possible drug-related ...problems. Under reporting of adverse drugs reactions are the global health problem. The adequate knowledge and skills towards pharmacovigilance and adverse drugs reactions reporting are crucial for the health care students to ensure patients’ medication safety. This study aims to assess the knowledge and attitude of the health care students towards pharmacovigilance and ADRs.
Methods: A closed ended, structured, self-administered questionnaire was administered to 204 undergraduate health care students to collect the data. Data were analyzed using SPSS version 21. Non-parametric tests (Mann Whitney U test and Kruskal Wallis test) were used for analysis.
Results: Among 204 respondents, the majority of them had a poor knowledge (91.18%) and positive attitude (87.25%) towards Pharmacovigilance. The inter quartile range (median) score of the respondents’ knowledge was 5.0±2.211 and attitude was 27.0±2.88 towards Pharmacovigilance and ADRs reporting. The main reason for under reporting of ADRs was difficulty to decide whether ADR has occurred or not (32.4%) due to the lack of appropriate knowledge and training. There is a poor knowledge and positive attitude towards Pharmacovigilance.
Conclusions: Adequate coverage of Pharmacovigilance and ADRs reporting issues should be covered in the curriculum as well as hand on training and workshop should be conducted to increase the knowledge and confidence in detecting, monitoring and reporting ADR in their clinical posting.
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