Children’s mental representations with respect to caregivers and post-traumatic symptomatology in Somatic Symptom Disorders and Disruptive Behavior Disorders

Introduction: In line with literature, the quality of adult-infant interactions and mental representations of the caregivers play an essential role in influencing the children's well-being. Many studies focused the attention on the role of attachment for a better evaluation of child psychopathological outcomes. The flexibility of the child's attachment model gives the opportunity to parents to be helped in modifying their own caregiving quality, encouraging the reflection on the children's state of mind with respect to attachment. The aims of this study were to evaluate: (1) the attachment models in young patients diagnosed with Disruptive Behavior Disorders (DBDs) and Somatic Symptoms Disorders (SSDs); (2) the levels of post-traumatic symptomatology; (3) the association between the attachment models and post-traumatic symptomatology. Methods: Forty Italian patients, aged from 8 to 15, recruited at Gaslini Paediatric Hospital of Genoa, previously diagnosed with SSD (N = 20) and DBD (N = 20) were assessed using the Child Attachment Interview (CAI), the Separation Anxiety Test (SAT), the Trauma Symptom Checklist for Children (TSCC-A). Socio-demographic data were collected. Results: In both the clinical samples, the findings on the distribution of attachment models showed a significant presence of insecure attachment with respect to both parents in more than a half of the patients and high levels of disorganized attachment. No significant differences between DBD and SSD samples were found on post-traumatic symptomatology (Post-Traumatic Stress and Dissociation). Significant differences were found on Depression, Anxiety, and Fantasy subscales. Discussion: This study can provide a detection of dysfunctional aspects in clinical populations. The findings suggest that the quality of the attachment to parents may be a fundamental element to better assess SSD and DBD in children and adolescents. Clinical implications of this study aimed at improving parental caregiving are highlighted

Mobile health (m-Health) for diabetes management

Diabetes is a major health challenge with a global impact regardless of age, country or economic condition. The increased prevalence of diabetes is reaching alarming levels. The necessity and urgency to find innovative care delivery solutions is becoming more important, particularly in the digital age. It is expected in the near future that more people with diabetes, especially the younger generations will be empowered by their smartphones and relevant mobile health (m-Health) innovations, to take more responsibility of their condition. Clinicians and healthcare providers are increasingly likely to assume the role of ‘navigators’ and ‘advisors’ rather than simply the medical gatekeeper for their patients. In this article, we describe the general architecture of current m-Health systems and applications for diabetes management. We also discuss the clinical evidence for impact from these important and innovative approaches to diabetes self-care and management and likely future trends in their usage. The latest statistics indicate that there are more than 1200 diabetes smartphone ‘apps’ and this area is growing exponentially in terms of ideas, technologies, devices and the associated industry. M-Health for diabetes care is now a major business stream for the medical device, mobile phone and IT telecommunication industries with high expectations arising from the potential benefits to be gained by both patients and healthcare providers. However, this potential has not yet been fully developed on the clinical side. This may be due to many factors including the reluctance of clinicians to engage with these technologies due to the lack of clinical evidence for their efficacy, poor adherence of people with diabetes to long-term use of these apps and the reluctance of healthcare funders to reimburse mobile diabetes

Detection, characterization and sizing of hydrogen induced cracking in pressure vessels using phased array ultrasonic data processing

Pressure vessels operating in sour service conditions in refinery environments can be subject to the risk of H₂S cracking resulting from the hydrogen entering into the material. This risk, which is related to the specific working conditions and to the quality of the steel used, shall be properly managed in order to maintain the highest safety at a cost-effective level. Nowadays the typical management strategy is based on a risk based inspection (RBI) evaluation to define the inspection plan used in conjunction with a fitness for service (FFS) approach in defining if the vessel, although presenting dangerous defects such as cracks, can still be considered “fit for purpose” for a given time window based on specific fracture mechanics analysis. These vessels are periodically subject to non-destructive evaluation, typically ultrasonic testing. Phased Array (PA) ultrasonic is the latest technology more and more used for this type of application. This paper presents the design and development of an optimized Phased Array ultrasonic inspection technique for the detection and sizing of hydrogen induced cracking (HIC) type flaws used as reference for comparison. Materials used, containing natural operational defects, were inspected in “as-service” conditions. Samples have then been inspected by means of a “full matrix capture” (FMC) acquisition process followed by “total focusing method” (TFM) data post processing. FCM-TFM data have been further post-processed and then used to create a 3D geometrical reconstruction of the volume inspected. Results obtained show the significant improvement that FMC/TFM has over traditional PA inspection techniques both in terms of sensitivity and resolution for this specific type of defect. Moreover, since the FMC allows for the complete time domain signal to be captured from every element of a linear array probe, the full set of data is available for post-processing. Finally, the possibility to reconstruct the geometry of the component from the scans, including the defects present in its volume, represents the ideal solution for a reliable data transferring process to the engineering function for the subsequent FFS analysi

Fuzzy Logic and Neuro-Fuzzy Networks for Environmental Hazard Assessment

Pollution and management of the environment are serious problems which concern the entire planet; the main responsibility should be attributed to human activities that contribute significantly to damage the environment, leading to an imbalance of natural ecosystems. In recent years, numerous studies focused on the three environmental compartments: soil, water and air. The pollution of groundwater is a widespread problem. The causes of pollution are often linked to human activities, including waste disposal. Solid waste management has become an important environmental issue in industrialized countries. The most serious problems are related to solid waste disposal. Landfill is still the most used disposal technique but not the safest. In fact, even controlled landfills could easily incur in the breakdown of containment elements. This breakdown could cause contamination of aquifer that is environmental pollution. Such contamination can be mitigated by performing remediation and environmental restoration. The assessment of environmental pollution risk can be performed with different degrees of detail and precision. Various statistical and mathematical models can be used for a qualitative risk assessment. The planning of a program for environmental remediation and restoration can be supported by expeditious methodologies that allow to obtain a hierarchical classification of contaminated sites. The literature offers some expeditious and qualitative methods including fuzzy logic (Zadeh, 1965), neural networks and neuro-fuzzy networks, which are more objective methods. The three artificial intelligence systems differ among themselves in some respects: fuzzy inference system learns knowledge of data only through the fuzzy rules; neural network is able to learn knowledge of data using the weights of synaptic connections; neuro-fuzzy systems are able to learn knowledge of neural data with neural paradigm and represent it in the form of fuzzy rules. Fuzzy logic was founded in 1965 by Zadeh. The first applications date back to the nineties. They were mainly used to control industrial processes, household electrical appliances and means of transport. Later, this approach was used in several fields including the environment. In fact it could be used for assessing environmental risk related to contamination of groundwater. The fuzzy approach is advantageous because it allows a quick assessment of the risk, but is disadvantageous because of the increasing complexity in the definition of fuzzy rules along with the increasing of the number of parameters. In many situations, when the number of parameters are considered high in the analysis, application of these techniques is cumbersome and complex and could be used for neuro-fuzzy models. These models reduce the complexity because they use training data. The neuro-fuzzy model were supported by a sensitivity analysis in order to address the problem of subjectivity and uncertainty of model input data

Engineered quantum tunnelling in extended periodic potentials

Quantum tunnelling from a tilted, but otherwise periodic potential is studied. Our theoretical and experimental results show that, by controlling the system's parameters, we can engineer the escape rate of a Bose-Einstein condensate to an exceptional degree. Possible applications of this atom-optics realization of the open Wannier-Stark system are discussed.Comment: 6 pp, proceedings DICE 11-15 September 2006, Castello di Piombino, Tuscany, Ital

On the threshold of effective well infant nursery hearing screening in Western Sicily

Objective: To determine the feasibility and effectiveness of well-infant nursery hearing screening programme for the early identification of hearing impairment, based on transient evoked otoacoustic emission (TEOAE) with a high “screen sensitivity” reducing the number of more expensive secondary level exams. Methods: The newborns were screened by non-specialist health workers in well babies nursery at the twentieth day of life for 6 years consecutive. Based on PASS/FAIL criteria and presence/absence of audiological risk factors the newborns were divided into four groups each one with its personal step program: G1- PASS without risk factor, free to go home; G2 - PASS with risk factor, retest at the age of 7 months; G3 - FAIL without risk factor, re-screening after 2 weeks for a maximum of four times before audiology assessment; G4 - FAIL with risk factor, retest after 2 weeks. Results: The coverage rate increased progressively from 89.8% to 92%. The referral rate was 1.51% after second stage with a specificity value of 98.78%. The four-stage screening performed for G3 reduced the numbers of global audiology assessment to 0.91% with a final global specificity of 99.4+0.4%. Conclusion: Less than 1% of infants underwent audiological assessment; the false positives resulted 0.62% with hearing loss global incidence of 2.95/1000 and a mean age of confirmation of 3.5 months of age. It is reasonable to think that this screening program could be implement to overall forty-two Western Sicily birth centres within few years