Empirical convergence analysis of federated averaging for failure prognosis

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented.This research was funded by the EPSRC and BT Prosperity Part- nership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/

Distributed diagnostics, prognostics and maintenance planning: Realizing industry 4.0

In this paper, a novel distributed yet integrated approach for diagnostics and prognostics is presented. An experimental study is conducted to validate the performance. Results showed that distributed prognostics give better performance in leaser computational time. Also, the proposed approach helps in making the results of the machine learning techniques comprehensible and more accurate. These results will be handy in arriving at predictive maintenance schedule considering the criticality of the system, the dependency of the components, available maintenance resources and confidence level in the results of the prognostic.Royal Academy of Engineering London, UK (IAPP 18-19/31

Product quality driven auto-prognostics: Low-cost digital solution for SMEs

Setting out existing prognostics solutions in small and medium enterprises (SMEs) is accompanied by challenges. These include employing expensive sensors, acquisition systems; and attending geometric limitations. Additionally, these solutions call for a specialist to take on feature engineering, machine learning algorithm selection, etc. Presented in this paper is a low-cost digital solution (intelligently integrate cost-cutting off-the-shelf technologies) for SMEs via product quality driven auto-prognostics. First, we develop upon existing solutions by addressing their drawbacks viz. cost, geometric limitations via a new product quality-centered condition monitoring strategy. Every SME must investigate the quality of their products, and therefore the authors believe this to be a low-cost solution. Next, the proposed solution integrates automated machine learning via Auto-WEKA, an off-the-shelf open-source technology. Lastly, the practical advantages of the proposed solution over the existing sensor-based solution were investigated via a case study. Results depict that this low-cost prognostics solution is vital for maintenance planning in SMEs.Royal Academy of Engineering London, UK (IAPP 18-19/31)

Secure and communications-efficient collaborative prognosis

Collaborative prognosis is a technique that is used to enable assets to improve their ability to predict failures by learning from the failures of similar other assets. This is typically made possible by enabling the assets to communicate with each other. The key enabler of current collaborative prognosis techniques is that they require assets to share their sensor data and failure information between each other, which might be a major constraint due to commercial sensitivities, especially when the assets belong to different companies. This paper uses Federated Learning to address this issue, and examines whether this technique will enable collaborative prognosis while ensuring sensitive operational data is not shared between organisational boundaries. An example implementation is demonstrated for prognosis of a simulated turbofan fleet, where Federated Averaging algorithm is used as an alternative for the data exchange step. Its performance is compared with conventional collaborative prognosis that involves failure data exchange. The results confirm that Federated Averaging retains the performance of conventional collaborative prognosis, while eliminating the exchange of failure data within assets. This removes a critical hinderance in industrial adoption of collaborative prognosis, thus enhancing the potential of predictive maintenance

ALTERNATIVE HERBAL DRUGS USED FOR TREATING HAIR DISEASE

Objective: The main objective of present study is to treat Alopecia. Alopecia areata is an unpredictable hair-loss condition. Alopecia is a dermatological disorder with psychosocial implications on patients with hair loss. Herbal systems of medicine have become increasingly popular in recent years. Medicinal plants have been used for the treatment of hair diseases since antiquity. Herbs such as turmeric, fenugreek, ginger, Cyperus rotundus (Nagarmotha), and holy basil are integral parts of ayurvedic formulations. Cyperus rotundus is a well-known ayurvedic herb with purported claims of hair growth promotion. Methods: Extracts are prepared by separating the soluble matter from vegetable tissues by the application of a suitable solvent such as alcohol, water, or ether. The resultant liquid is concentrated by evaporation to obtain a liquid extract or concentrated almost to dryness to obtain the solid extract and its volatile oil. Hair formulation of Cyperus rotundus belonging to family Cyperaceae in the form of herbal formulation (5% herbal cream and oil) was studied and it showed excellent hair growth activity with standard (2% minoxidil ethanolic solution) in Wister albino rats. Results: Hair growth initiation time was significantly reduced to half on treatment with the oil, as compared to control animals. The hair growth promoting effect was evaluated against the control, standard, and test animals at 0, 10, 15, and 20 days with the formulated hair oil and hair cream prepared from the volatile oil extracted from the Cyperus rotundus and the significant hair growth was observed, and the growth was compared with the standard drug used 2% solution of minoxidil. Conclusion: The results of treatment with oil were better than the positive control minoxidil 2% treatment. It holds the promise of potent herbal alternative for minoxidil. Keywords: Alopecia areata, Cyperus rotundus, Hair growth, Extracts, Herbal creams, Hair oil, Hair formulation, Ayurvedic, Cyperaceae.  ORCID iD: - http://orcid.org/0000-0002-0439-606

Detection and confirmation of toxigenic Vibrio cholerae O1 in environmental and clinical samples by a direct cell multiplex PCR

Epidemic cholera caused by toxigenic Vibrio cholerae O1 is a major health problem in several developing countries. Traditional methods for identifying V. cholerae involve cultural, biochemical and immunological assays which are cumbersome and often take several days to complete. In the present study, a direct cell multiplex PCR was developed targeting the ompW, ctxB and rfbO1 genes for confirmation of V. cholerae, its toxigenicity and serogroup O1, respectively from clinical and environmental samples. The detection sensitivity of the multiplex PCR was 1.9 x 103 V. cholerae per PCR reaction. A total of 31 environmental samples and 45 clinical V. cholerae isolates from different outbreaks were examined by the PCR. The assay was simple and specific, as there was no requirement for DNA extraction and no amplification was  observed with other homologous bacteria used. The assay can be very useful for rapid surveillance of toxigenic V. cholerae O1 in environmental water samples, as well as for confirmation of clinical isolates.Keywords: cholera, Vibrio cholerae, PCR, environmental sample

STABILITY INDICATING REVERSED PHASE-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY METHOD DEVELOPMENT AND VALIDATION FOR SIMULTANEOUS DETERMINATION OF RELATED SUBSTANCES OF CITICOLINE AND PIRACETAM IN PHARMACEUTICAL DOSAGE FORM

ABSTRACTObjective: A high-performance liquid chromatography (HPLC) method was developed and validated to determine stability indicating method ofPiracetam and Citicoline in a tablet dosage.Methods: The separation was made using Inertsil C18, 250 × 4.6 mm, 5 µm column, mobile phase used contained phosphate buffer and acetonitrilein the gradient mode at wavelength of 205 nm for Piracetam and 280 nm for Citicoline on a PDA detector.Results: The method showed good linearity for, respectively related substances of Citicoline and Piracetam with correlation coefficients in the rangeof 0.29-623 µg/mL and 0.48-1030 µg/mL, respectively. Method accuracy was assessed at three levels; the recovery ranged between 100.0% and 102%for Citicoline and for Piracetam between 94.3% and 109.1%. Limit of detection and quantification for Citicoline was 0.07 µg/mL - 0.25 µg/ml and forPiracetam 0.12 µg/mL - 0.41 µg/ml. The solution was found to be stable within 27 hrs at room temperature.Conclusion: The method was demonstrated to be robust and simple, and suitable for industrial application for determination of related substancesin the pharmaceutical preparation.Keywords: Piracetam, Citicoline, Assay, Reversed phase-high performance liquid chromatography

Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques

Solving kk-means on High-dimensional Big Data

In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the $k$-means problem, this has led to the development of several $(1+\varepsilon)$-approximations (under the assumption that $k$ is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits. We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream. We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on Experimental Algorithms - SEA 201

STATS - A Point Access Method for Multidimensional Clusters.

The ubiquity of high-dimensional data in machine learning and data mining applications makes its efficient indexing and retrieval from main memory crucial. Frequently, these machine learning algorithms need to query specific characteristics of single multidimensional points. For example, given a clustered dataset, the cluster membership (CM) query retrieves the cluster to which an object belongs. To efficiently answer this type of query we have developed STATS, a novel main-memory index which scales to answer CM queries on increasingly big datasets. Current indexing methods are oblivious to the structure of clusters in the data, and we thus, develop STATS around the key insight that exploiting the cluster information when indexing and preserving it in the index will accelerate look up. We show experimentally that STATS outperforms known methods in regards to retrieval time and scales well with dataset size for any number of dimensions