Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets

Detecting anomalies over real-world datasets remains a challenging task. Data annotation is an intensive human labor problem, particularly in sequential datasets, where the start and end time of anomalies are not known. As a result, data collected from sequential real-world processes can be largely unlabeled or contain inaccurate labels. These characteristics challenge the application of anomaly detection techniques based on supervised learning. In contrast, Multiple Instance Learning (MIL) has been shown effective on problems with incomplete knowledge of labels in the training dataset, mainly due to the notion of bags. While largely under-leveraged for anomaly detection, MIL provides an appealing formulation for anomaly detection over real-world datasets, and it is the primary contribution of this paper. In this paper, we propose an MIL-based formulation and various algorithmic instantiations of this framework based on different design decisions for key components of the framework. We evaluate the resulting algorithms over four datasets that capture different physical processes along different modalities. The experimental evaluation draws out several observations. The MIL-based formulation performs no worse than single instance learning on easy to moderate datasets and outperforms single-instance learning on more challenging datasets. Altogether, the results show that the framework generalizes well over diverse datasets resulting from different real-world application domains.Comment: 9 pages,5 figures, Anomaly and Novelty Detection, Explanation and Accommodation (ANDEA 2022

The effect of peer education on treating pain in patients for burn debridement

Introduction: Debridement is a daily care for burn patients that can cause severe pain due to skin damage. Pain is one of the primary side effects of burn wounds and relieving pain is a basic need for all patients. Aim: To investigate the impact of peer education on the pain level of patients for burn debridement. Materials and Methods: This clinical trial conducted from January 2014 to March 2015, consisted of 60 patients who were to undergo burn debridement. The patients in the control group received routine training regarding the methods to reduce pain and the patients in the intervention group were trained by their peers under the supervision of the researcher. Pain severity was re-evaluated in both the groups on that day after training. The data collection tool was the demographic information questionnaire and a Visual Analogue Pain Scale (VAS). Data were analysed using SPSS software (version 18) and descriptive-analytical tests. Results: The mean score of pain severity at the beginning of burn debridement was 6.35±2.10 in the intervention group and 5.30±1.85 in the control group. After the peer education, the mean score of pain severity was 3.30±1.78 and 4.20±1.23 in the intervention and control group, respectively (p-value=0.02). Conclusion: Peer education can significantly reduce the severity of pain associated with burn debridement. The use of non pharmacological effective techniques, such as peer education can be beneficial in relieving pain and preventing its exacerbation. © 2018, Journal of Clinical and Diagnostic Research. All rights reserved

Micellar structure and transformations in sodium alkylbenzenesulfonate (NaLAS) aqueous solutions: effects of concentration, temperature, and salt

We investigate the shape, dimensions, and transformation pathways of micelles of linear sodium alkylbenzenesulfonate (NaLAS), a common anionic surfactant, in aqueous solution. Employing Small Angle Neutron Scattering (SANS) and surface tensiometry, we quantify the effects of surfactant concentration (0.6–15 wt%), temperature (5–40 °C) and added salt (≤0.35 M Na2SO4). Spherical micelles form at low NaLAS (≤2.6 wt%) concentration in water, and become elongated with increasing concentration and decreasing temperature. Addition of salt reduces the critical micelle concentration (CMC) and thus promotes the formation of micelles. At fixed NaLAS concentration, salt addition causes spherical micelles to grow into cylindrical micelles, and then multilamellar vesicles (MLVs), which we examine by SANS and cryo-TEM. Above a threshold salt concentration, the MLVs reach diameters of 100 s of nm to few μm, eventually causing precipitation. While the salt concentrations associated with the micelle-to-cylinder transformation increase only slightly with temperature, those required for the cylinder-to-MLV transformation exhibit a pronounced, linear temperature dependence, which we examine in detail. Our study establishes a solution structure map for this model anionic surfactant in water, quantifying the combined roles of concentration, temperature and salt, at practically relevant conditions

A review on the heat and mass transfer phenomena in nanofluid coolants with special focus on automotive applications

Engineered suspensions of nanosized particles (nanofluids) are characterized by superior thermal properties. Due to the increasing need for ultrahigh performance cooling in many industries, nanofluids have been widely investigated as next-generation coolants. However, the multiscale nature of nanofluids implies nontrivial relations between their design characteristics and the resulting thermo-physical properties, which are far from being fully understood. This pronounced sensitivity is the main reason for some contradictory results among both experimental evidence and theoretical considerations presented in the literature. In this Review, the role of fundamental heat and mass transfer mechanisms governing thermo-physical properties of nanofluids is assessed, from both experimental and theoretical point of view. Starting from the characteristic nanoscale transport phenomena occurring at the particle-fluid interface, a comprehensive review of the influence of geometrical (particle shape, size and volume concentration), physical (temperature) and chemical (particle material, pH and surfactant concentration in the base fluid) parameters on the nanofluid properties was carried out. Particular focus was devoted to highlight the advantages of using nanofluids as coolants for automotive heat exchangers, and a number of design guidelines was suggested for balancing thermal conductivity and viscosity enhancement in nanofluids. This Review may contribute to a more rational design of the thermo-physical properties of particle suspensions, therefore easing the translation of nanofluid technology from small-scale research laboratories to large-scale industrial applications

On inference in a class of exponential distribution under imperfect maintenance

International audienceThis paper deals with statistical inference for lifetime data in presence of imperfect maintenance. For the maintenance model, the Sheu and Griffith model is considered. The lifetime distribution belongs to exponential distribution class. The maximum likelihood estimation procedure of the model parameters is discussed, and confidence intervals are provided using the asymptotic likelihood theory and bootstrap approach. Based on conjugate and discrete priors, Bayesian estimators of the model parameters are developed under symmetric and asymmetric loss functions. The proposed methodologies are applied to simulated data and sensitivity analysis to different parameters and data characteristics is carried out. The effect of model misspecification is also assessed within this class of distributions through a Monte Carlo simulation study. Finally, two datasets are analyzed for demonstrative aims

Effect of Sewage-Sludge on Bioremediation of a Crude-Oil Polluted Soil

Khuzestan Province accommodates the largest oil-fields with huge petroleum production in Iran. During the Persian Gulf war in 1991, more than 6-8 million gallons of oil was spilt in the Persian Gulf, the greatest amount of which was transported into Khuzestan soil. Thus, oil removal from contaminated soil by advanced technologies such as bioremediation seems to be of vital necessity. The aim of this study was to evaluate the effect of sewage-sludge application on bioremediation of oil-contaminated soil. Soil samples (5kg) were artificially contaminated with crude oil to a level of 1000 mg/kg. Sewage sludge treatments were applied at the 3 levels of 0, 100, and 200 gr/5kg soil in 3 replicates. The soils were kept in the normal moisture aerobic environment for 5 and 10 weeks. The soils were then analyzed for Hydrocarbon-degrading heterotrophic bacterial count. Oil extraction from the samples was accomplished using the oil Soxhlet extraction method and oil degradation was measured by GC chromatography. The results showed that the hydrocarbon-degrading and heterotrophic bacterial counts in all the treatments increased with time. Results indicate that heterotrophic bacterial population increased from 6×103 cfu/gr soil to  2×1010  cfu/gr soil. Also, C/N ratio decreased from 6 to 3. GC results indicated that all normal Alkanes and Isopernoids, i.e. Phytane and Pristane, decreased by 50-90 percent in all the treatments. It was also found that the application of sewage sludge at 100 gr/5kg soil to oil-contaminated soil leads to greater rates of biodegradation after 5 week

Analysis of time-to-failure data for a repairable system subject to degradation

International audienceIn this paper, a gradually deteriorating system with imperfect repair is considered. The deterioration is modeled by a stationary stochastic process. The system fails once the deterioration level exceeds a given threshold L. At failure, an imperfect repair is performed and the deterioration level is reduced to a fixed value r, say. The system can be repaired n − 1 times and will be replaced after the nth failure. The article aims to estimate the parameters of the proposed deterioration process based on the observed failures. To this end we consider the Wiener and Gamma processes which are the most common used stochastic process models. In Wiener process, an explicit expression for the estimators is obtained. Birnbaum-Saunders approximation is extended to estimate the parameters in Gamma process. An optimal replacement policy is also discussed. Finally, a Monte-Carlo simulation is conducted to investigate the performance of estimators

The effect of peer education on treating pain in patients for burn debridement

Introduction: Debridement is a daily care for burn patients that can cause severe pain due to skin damage. Pain is one of the primary side effects of burn wounds and relieving pain is a basic need for all patients. Aim: To investigate the impact of peer education on the pain level of patients for burn debridement. Materials and Methods: This clinical trial conducted from January 2014 to March 2015, consisted of 60 patients who were to undergo burn debridement. The patients in the control group received routine training regarding the methods to reduce pain and the patients in the intervention group were trained by their peers under the supervision of the researcher. Pain severity was re-evaluated in both the groups on that day after training. The data collection tool was the demographic information questionnaire and a Visual Analogue Pain Scale (VAS). Data were analysed using SPSS software (version 18) and descriptive-analytical tests. Results: The mean score of pain severity at the beginning of burn debridement was 6.35±2.10 in the intervention group and 5.30±1.85 in the control group. After the peer education, the mean score of pain severity was 3.30±1.78 and 4.20±1.23 in the intervention and control group, respectively (p-value=0.02). Conclusion: Peer education can significantly reduce the severity of pain associated with burn debridement. The use of non pharmacological effective techniques, such as peer education can be beneficial in relieving pain and preventing its exacerbation. © 2018, Journal of Clinical and Diagnostic Research. All rights reserved