Buddhist-Derived Loving-Kindness and Compassion Meditation for the Treatment of Psychopathology: a Systematic Review

Although clinical interest has predominantly focused on mindfulness meditation, interest into the clinical utility of Buddhist-derived loving-kindness meditation (LKM) and compassion meditation (CM) is also growing. This paper follows the PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines and provides an evaluative systematic review of LKM and CM intervention studies. Five electronic academic databases were systematically searched to identify all intervention studies assessing changes in the symptom severity of Diagnostic and Statistical Manual of Mental Disorders (text revision fourth edition) Axis I disorders in clinical samples and/or known concomitants thereof in sub-clinical/healthy samples. The comprehensive database search yielded 342 papers and 20 studies (comprising a total of 1,312 participants) were eligible for inclusion. The Quality Assessment Tool for Quantitative Studies was then used to assess study quality. Participants demonstrated significant improvements across five psychopathology-relevant outcome domains: (i) positive and negative affect, (ii) psychological distress, (iii) positive thinking, (iv) interpersonal relations, and (v) empathic accuracy. It is concluded that LKM and CM interventions may have utility for treating a variety of psychopathologies. However, to overcome obstacles to clinical integration, a lessons-learned approach is recommended whereby issues encountered during the (ongoing) operationalization of mindfulness interventions are duly considered. In particular, there is a need to establish accurate working definitions for LKM and CM

Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction

Modeling the Effects of Maintenance on the degradation of a Water-feeding Turbo-pump of a Nuclear Power Plant

International audienceThis work addresses the modelling of the effects of maintenance on the degradation of an electric power plant component. This is done within a modelling framework previously proposed by the authors, of which the distinguishing feature is the characterization of the component living conditions by influencing factors (IFs), i.e. conditioning aspects of the component life that influence its degradation. The original fuzzy logic-based modelling framework includes maintenance as an IF; this requires one to jointly model its effects on the component degradation together with those of the other influencing factors. This may not come natural to the experts who are requested to provide the if-then linguistic rules at the basis of the fuzzy model linking the IFs with the component degradation state. An alternative modelling approach is proposed in this work, which does not consider maintenance as an IF that directly impacts on the degradation but as an external action that affects the state of the other IFs. By way of an example regarding the propagation of a crack in a water-feeding turbo-pump of a nuclear power plant, the approach is shown to properly model the maintenance actions based on information that can be more easily elicited from experts

An analysis of roe deer (Capreolus capreolus) traffic collisions in the Belluno province, eastern Italian Alps

Data of roe deer traffic collisions from 1989 to 2004 in the Belluno province were analyzed to describe patterns of road kills by zone, season and sex and to compare resulting annual trends and sex ratios with those estimated for roe deer population. The province was divided in 2 districts on the base of differences in climate, landscape and roe deer population status. Pearson's simple correlation was used to investigate the associations, in the two districts, among road kills data, year, population density, traffic index, and snow depth. Bonferroni's confidential intervals to 95% of significance were used to compare the monthly distributions of collisions between sexes and between districts. In conclusion, the analysis of car accidents may not reflect population trends and sex ratios when traffic rates change and when different ecological factors, others from deer density, influence the probability of deer to incur in a car accident. In addition, differences of accident probability between sexes and months can be found in areas with different landscapes, climates and population structures. These factors should be evaluated in order to manage accident risk and to understand the potential of car accidents records as a tool for monitoring population status

Wild boar (Sus scrofa) damages to mountain grassland. A case study in the Belluno province, eastern Italian Alps

Five alpine pastures (34±14ha) of the Belluno province, patchily damaged by wild boar, were chosen to investigate on main environmental parameters that might influence the rooting sites selection. Eighty damage sites were examined. For each damage surrounding type of grassland and distance from woodland were recorded and mapped using a G.I.S. software. Proportional availability (% of total pasture surface) of grassland types (rough grass, rich grass, degraded, shrubs and trees) and classes of distance from woodland (120m), and the respective use (% of total damage events) were estimated and compared with Chi-square test. A selection index was calculated as use/availability and Bonferroni confidence intervals (95%) were used to test significance. Degraded pastures and areas closer to woodland blocks were preferred. A vegetational analysis inside and outside the damaged areas showed a reduction in frequency of species producing bulbs and rhizomes, and in Poaceae as respect to other families of lower forage value. Future studies should investigate the role of different root forms, and invertebrate richness, on rooting site selection. Longer term studies are also needed to better define the evolution of pasture botanical composition of damaged areas

Non-Steroidal Anti-Inflammatory Drugs in the Carcinogenesisof the Gastrointestinal Tract

It is estimated that underlying infections and inflammatory responses are linked to 15–20% of all deaths from cancer worldwide. Inflammation is a physiologic process in response to tissue damage resulting from microbial pathogen infection, chemical irritation, and/or wounding. Tissues injured throughout the recruitment of inflammatory cells such as macrophages and neutrophils, generate a great amount of growth factors, cytokines, and reactive oxygen and nitrogen species that may cause DNA damage that in turn predisposes to the transformation from chronic inflammation to neoplasia. Cyclooxygenase (COX), playing a key role in cell homeostasis, angiogenesis and tumourigenesis, may represent the link between inflammation and cancer. Currently COX is becoming a pharmacological target for cancer prevention and treatment

Identification of Contradictory Patterns in Experimental Datasets for the Development of Models for Electrical Cables Diagnostics

International audienceThe state of health of an electrical cable may be difficult to know, without destructive or very expensive tests. To overcome this, partial discharge (PD) measurements have been proposed as a relatively economic and simple-to-apply experimental technique for retrieving information on the state of health of an electrical cable. The retrieval is based on a relationship between PD measurements and the health of the cable. Given the difficulties in capturing such relationship by analytical models, empirical modeling techniques based on experimental data have been propounded. In this view, a set of PD measurements have been collected by Enea Ricerca sul Sistema Elettrico-ERSE during past campaigns, for building a diagnostic system of electrical cable health state. These experimental data may contain contradictory information which remarkably reduces the performance of the state classifier, if not a priori identified and possibly corrected. In the present paper, a novel technique based on the Adaboost algorithm is proposed for identifying contradictory PD patterns within an a priori analysis aimed at improving the diagnostic performance. Adaboost is a bootstrap-inspired, ensemble-based algorithm which has been effectively used for addressing classification problems

Risk assessment of a bulk cryogenic tank: Beyond the Leak-Before-Break criterion

International audienceThe increase in the size and production capacity of air separation plants has boosted the need of developing methodologies to properly assess the risk related to major releases of liquefied gas. In this respect, the Leak-Before-Break (LBB) assessment is currently adopted to demonstrate the safety of the structures containing liquefied gas, under the assumption that the tank is always operated in nominal conditions. This assumption is questioned in this paper, which proposes a new methodology for the assessment of the risks related to cryogenic tank catastrophic rupture. The methodology provides a comprehensive understanding of the issues associated to the worst case rupture scenario: from the investigation of the causes of the undesirable operating conditions up to the analysis of the associated structural consequences, within a probabilistic framewo

A reinforcement learning framework for optimal operation and maintenance of power grids

International audienc