Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis

Response of Garlic to Organic and Inorganic Fertilizers

An experiment was carried out to study the response of organic and inorganic fertilizers on growth, yield and quality of garlic (Allium sativum L.) cv. Yamuna Safed-3. The results revealed that the combined application of 25% RDF with 75% N through FYM @ 20 t/ha gave higher marketable bulb yield of 19.34t/ha as compared to other treatments which were statistically on par with 100% RDF (18.53 t/ha ) and 50% RDF + 50% N supplied as FYM (18.94 t/ha). It is suggested that for better biometric observations, bulb characters and marketable bulb yield in garlic, combined use of inorganic and organic source of nutrient supply is preferable

Magnetic behavior of nano crystals of a spin-chain system, Ca3Co2O6: Absence of multiple steps in the low temperature isothermal magnetization

We report that the major features in the temperature dependence of dc and ac magnetization of a well-known spin-chain compound, Ca3Co2O6, which has been known to exhibit two complex magnetic transitions due to geometrical frustration (one near 24 K and the other near 10 K), are found to be qualitatively unaffected in its nano form synthesized by high-energy ball-milling. However, the multiple steps in isothermal magnetization - a topic of current interest in low-dimensional systems - known for the bulk form well below 10 K is absent in the nano particles. We believe that this finding will be useful to the understanding of the 'step' magnetization behavior of such spin-chain systems.Comment: Phys. Rev. B (Rapid Communications), in pres

Substrate finishing and niobium content effects on the high temperature corrosion resistance in steam atmosphere of CrN/NbN superlattice coatings deposited by PVD-HIPIMS

The main objective of this work was to evaluate the oxidation resistance of three PVD-HIPIMS CrN/NbN coatings, studying the effect of the surface finishing of the substrate and the role of niobium content into the coating composition. CrN/NbN nano-multilayered films on P92 steel were tested at 650°C in pure steam atmosphere. The mass gain was measured at fixed intervals to study their oxidation kinetics. The morphology and thickness of nanoscales were measured by transmission electron microscopy (TEM). Characterization of coatings before and after the thermal treatment was performed by scanning electron microscopy-energy with facilities of dispersive X-ray spectroscopy (SEM–EDX) and X-ray diffraction (XRD). All coatings improved the oxidation resistance of the substrate material, but the best behaviour was exhibited by the CrN/NbN with the high niobium (Nb) content and deposited on the substrate with the finest surface finishing

Deposition of nanoscale multilayer CrN/NbN physical vapor deposition coatings by high power impulse magnetron sputtering

Nanoscale multilayer CrN/NbN physical vapor deposition (PVD) coatings are gaining reputation for their high corrosion and wear resistance. However, the CrN/NbN films deposited by ABS(TM) (are bond sputtering) technology have some limitations such as macrodroplets, porosity, and less dense structures. The novel HIPIMS (high power impulse magnetron sputtering) technique produces macroparticle-free, highly ionized metal plasma, which brings advantages in both surface pretreatment and coating deposition stages of the PVD process. In this study, nanoscale multilayer CrN/NbN PVD coatings were pretreated and deposited with HIPIMS technology and compared with those deposited by HIPIMS-UBM (unbalanced magnetron) and by the ABSTM technique. In all cases Cr+ etching was utilized to enhance adhesion by low energy ion implantation. The coatings were deposited at 400 degrees C with substrate biased (Ub) at -75 V. During coating deposition, HIPIMS produced significantly high activation of nitrogen compared to the UBM as observed with mass spectroscopy. HIPIMS-deposited coatings revealed a bilayer period of 4.1 nm (total thickness: 2.9 mu m) and hardness of 3025 HK0.025. TEM results revealed droplet free, denser microstructure with (200) preferred orientation for the HIPIMS coating owing to the increased ionization as compared to the more porous structure with random orientation observed in UBM coating. The dry sliding wear coefficient (K-c) of the coating was 1.8 X 10(-15) m(3) N-1 m(-1), whereas the steady state coefficient of friction was 0.32. Potentiodynamic polarization tests revealed higher E-corr values, higher pitting resistance (around potentials +400 to +600 mV), and lower. corrosion current densities for HIPIMS deposited coatings as compared to the coatings deposited by ABS or HIPIMS-UBM. The corrosion behavior of the coatings qualitatively improved with the progressive use of HIPIMS from pretreatment stage to the coating deposition step. (C) 2008 American Vacuum Society

COVID-19 deaths in people with intellectual disability in the UK and Ireland: descriptive study.

BACKGROUND: Rapid spread of coronavirus disease 2019 (COVID-19) has affected people with intellectual disability disproportionately. Existing data does not provide enough information to understand factors associated with increased deaths in those with intellectual disability. Establishing who is at high risk is important in developing prevention strategies, given risk factors or comorbidities in people with intellectual disability may be different to those in the general population. AIMS: To identify comorbidities, demographic and clinical factors of those individuals with intellectual disability who have died from COVID-19. METHOD: An observational descriptive case series looking at deaths because of COVID-19 in people with intellectual disability was conducted. Along with established risk factors observed in the general population, possible specific risk factors and comorbidities in people with intellectual disability for deaths related to COVID-19 were examined. Comparisons between mild and moderate-to-profound intellectual disability subcohorts were undertaken. RESULTS: Data on 66 deaths in individuals with intellectual disability were analysed. This group was younger (mean age 64 years) compared with the age of death in the general population because of COVID-19. High rates of moderate-to-profound intellectual disability (n = 43), epilepsy (n = 29), mental illness (n = 29), dysphagia (n = 23), Down syndrome (n = 20) and dementia (n = 15) were observed. CONCLUSIONS: This is the first study exploring associations between possible risk factors and comorbidities found in COVID-19 deaths in people with intellectual disability. Our data provides insight into possible factors for deaths in people with intellectual disability. Some of the factors varied between the mild and moderate-to-profound intellectual disability groups. This highlights an urgent need for further systemic inquiry and study of the possible cumulative impact of these factors and comorbidities given the possibility of COVID-19 resurgence

Collocation analysis for UMLS knowledge-based word sense disambiguation

BACKGROUND: The effectiveness of knowledge-based word sense disambiguation (WSD) approaches depends in part on the information available in the reference knowledge resource. Off the shelf, these resources are not optimized for WSD and might lack terms to model the context properly. In addition, they might include noisy terms which contribute to false positives in the disambiguation results. METHODS: We analyzed some collocation types which could improve the performance of knowledge-based disambiguation methods. Collocations are obtained by extracting candidate collocations from MEDLINE and then assigning them to one of the senses of an ambiguous word. We performed this assignment either using semantic group profiles or a knowledge-based disambiguation method. In addition to collocations, we used second-order features from a previously implemented approach.Specifically, we measured the effect of these collocations in two knowledge-based WSD methods. The first method, AEC, uses the knowledge from the UMLS to collect examples from MEDLINE which are used to train a Naïve Bayes approach. The second method, MRD, builds a profile for each candidate sense based on the UMLS and compares the profile to the context of the ambiguous word.We have used two WSD test sets which contain disambiguation cases which are mapped to UMLS concepts. The first one, the NLM WSD set, was developed manually by several domain experts and contains words with high frequency occurrence in MEDLINE. The second one, the MSH WSD set, was developed automatically using the MeSH indexing in MEDLINE. It contains a larger set of words and covers a larger number of UMLS semantic types. RESULTS: The results indicate an improvement after the use of collocations, although the approaches have different performance depending on the data set. In the NLM WSD set, the improvement is larger for the MRD disambiguation method using second-order features. Assignment of collocations to a candidate sense based on UMLS semantic group profiles is more effective in the AEC method.In the MSH WSD set, the increment in performance is modest for all the methods. Collocations combined with the MRD disambiguation method have the best performance. The MRD disambiguation method and second-order features provide an insignificant change in performance. The AEC disambiguation method gives a modest improvement in performance. Assignment of collocations to a candidate sense based on knowledge-based methods has better performance. CONCLUSIONS: Collocations improve the performance of knowledge-based disambiguation methods, although results vary depending on the test set and method used. Generally, the AEC method is sensitive to query drift. Using AEC, just a few selected terms provide a large improvement in disambiguation performance. The MRD method handles noisy terms better but requires a larger set of terms to improve performance