FINEMET-type thin films deposited by HiPIMS: Influence of growth and annealing conditions on the magnetic behaviour

Results concerning the influence of deposition conditions (effective power, P_eff, pulse length, τ, and working gas pressure, p) as well as of thermal treatments on the properties of Fe_73.5Cu₁Nb₃Si_15.5B₇ thin films, deposited by high power impulse magnetron sputtering (HiPIMS) technique, are presented. The P_eff, τ and p values were varied in the range of 30–90 W, 4–20 μs and 8–60 mTorr respectively. According to the XRD analysis, in as-deposited state, all the prepared samples are amorphous. For P_eff constant the coercive magnetic field, H_c, increases whit τ, while for τ constant H_c decreases when P_eff increases. The lowest H_c values have been obtained after the samples were annealed at temperatures between 450 °C and 480 °C, when the average size of the α-Fe(Si) grains and the crystalline volume fraction increase about 45% and 20% respectively.     

Fusing integrated visual vocabularies-based bag of visual words and weighted colour moments on spatial pyramid layout for natural scene image classification

The bag of visual words (BOW) model is an efficient image representation technique for image categorization and annotation tasks. Building good visual vocabularies, from automatically extracted image feature vectors, produces discriminative visual words, which can improve the accuracy of image categorization tasks. Most approaches that use the BOW model in categorizing images ignore useful information that can be obtained from image classes to build visual vocabularies. Moreover, most BOW models use intensity features extracted from local regions and disregard colour information, which is an important characteristic of any natural scene image. In this paper, we show that integrating visual vocabularies generated from each image category improves the BOW image representation and improves accuracy in natural scene image classification. We use a keypoint density-based weighting method to combine the BOW representation with image colour information on a spatial pyramid layout. In addition, we show that visual vocabularies generated from training images of one scene image dataset can plausibly represent another scene image dataset on the same domain. This helps in reducing time and effort needed to build new visual vocabularies. The proposed approach is evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results, using support vector machines with histogram intersection kernel, show that the proposed approach outperforms baseline methods such as Gist features, rgbSIFT features and different configurations of the BOW model.     

Aflatoxin M1 determination and stability study in milk samples using a screen-printed 96-well electrochemical microplate

A highly sensitive method for the determination of aflatoxin M₁ (AFM₁) is described that involves the use of a disposable multichannel microplate coupled to intermittent pulse amperometry (IPA) for immunosensor development based on a direct competitive assay. Immunoassay parameters were evaluated and optimized to establish an electrochemical enzyme-linked immunosorbent assay (ELISA) procedure for AFM₁ in milk samples. The suitability of the immunosensors for the direct analysis of the toxin in milk was assessed. AFM₁ was measured with a working range of 5–250 pg mL⁻¹ and a detection limit of 1 pg mL⁻¹. Good recovery values (90–105%) were obtained. The method was used to study the recovery of this toxin in frozen (−30 °C) or lyophilized (4 °C) milk samples stored for up to 3 months. A significant, but variable, decrease (>50%) of the measured AFM₁ concentration with respect to the initial toxin contamination levels was found.     

Action of some proteic and carbohydrate components of Symphytum officinale upon normal and neoplastic cells

OBJECTIVES: To describe the distribution of nosocomial infections among surgical patients by site of infection for different types of operations, and to show how the risk of certain adverse outcomes associated with nosocomial infection varied by site, type of operation, and exposure to specific medical devices. DESIGN: Surveillance of surgical patients during January 1986-June 1992 using standard definitions and protocols for both comprehensive (all sites, all operations) and targeted (all sites, selected operations) infection detection. SETTING AND PATIENTS: Acute care US hospitals participating in the National Nosocomial Infection Surveillance (NNIS) System: 42,509 patients with 52,388 infections from 95 hospitals using comprehensive surveillance protocols and an additional 5,659 patients with 6,963 infections from 11 more hospitals using a targeted protocol. RESULTS: Surgical site infection was the most common nosocomial infection site (37%) when data were reported by hospitals using the comprehensive protocols. When infections reported from both types of protocols were stratified by type of operation, other sites were most frequent following certain operations (e.g., urinary tract infection after joint prosthesis surgery [52%]). Among the infected surgical patients who died, the probability that an infection was related to the patient's death varied significantly with the site of infection, from 22% for urinary tract infection to 89% for organ/space surgical site infection, but was independent of the type of operation performed. The probability of developing a secondary bloodstream infection also varied significantly with the primary site of infection, from 3.1% for incisional surgical site infection to 9.5% for organ/space surgical site infection (p < .001). For all infections except pneumonia, the risk of developing a secondary bloodstream infection also varied significantly with the type of operation performed (p < .001) and was generally highest for cardiac surgery and lowest for abdominal hysterectomy. Surgical patients who developed ventilator-associated pneumonia were more than twice as likely to develop a secondary bloodstream infection as nonventilated pneumonia patients (8.1% versus 3.3%, p < .001). CONCLUSIONS: For surgical patients with nosocomial infection, the distribution of nosocomial infections by site varies by type of operation, the frequency with which nosocomial infections contribute to patient mortality varies by site of infection but not by type of operation, and the risk of developing a secondary bloodstream infection varies by type of primary infection and, except for pneumonia, by type of operation.     

Analogy-based software effort estimation using Fuzzy numbers

Background

Early stage software effort estimation is a crucial task for project bedding and feasibility studies. Since collected data during the early stages of a software development lifecycle is always imprecise and uncertain, it is very hard to deliver accurate estimates. Analogy-based estimation, which is one of the popular estimation methods, is rarely used during the early stage of a project because of uncertainty associated with attribute measurement and data availability.

Aims

We have integrated analogy-based estimation with Fuzzy numbers in order to improve the performance of software project effort estimation during the early stages of a software development lifecycle, using all available early data. Particularly, this paper proposes a new software project similarity measure and a new adaptation technique based on Fuzzy numbers.

Method

Empirical evaluations with Jack-knifing procedure have been carried out using five benchmark data sets of software projects, namely, ISBSG, Desharnais, Kemerer, Albrecht and COCOMO, and results are reported. The results are compared to those obtained by methods employed in the literature using case-based reasoning and stepwise regression.

Results

In all data sets the empirical evaluations have shown that the proposed similarity measure and adaptation techniques method were able to significantly improve the performance of analogy-based estimation during the early stages of software development. The results have also shown that the proposed method outperforms some well know estimation techniques such as case-based reasoning and stepwise regression.

Conclusions

It is concluded that the proposed estimation model could form a useful approach for early stage estimation especially when data is almost uncertain.     

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

Drug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables.     

Thermally stimulated currents in methacrylonitrile-styrene copolymer films

Summary Measurements are reported on thermally stimulated discharge current in methacrylonitrile — styrene copolymer films. The values of activation energy and relaxation times were calculated.     

Sulfur-Tolerant,Exsolved Fe–Ni Alloy Nanoparticles for CO Oxidation

Topics in Catalysis - Metallic nanoparticles exsolved at the surface of perovskite oxides have been recently shown to unlock superior catalytic activity and durability towards various chemical...     

Diagnostics and prognostics for complex systems: A review of methods and challenges

Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever-increasing complexity makes fault diagnostics and prognostics challenging for the system-level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on application to complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper first presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems. Afterwards, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state-of-the-art and case studies with a reference value for scholars and practitioners.