A Shannon entropy approach for structural damage identification based on self-powered sensor data

© 2019 Elsevier Ltd Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions

Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems

Factor analysis of self-treatment in diabetes mellitus: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Self-treatment is a treatment of oneself without professional help, which may cause health-related consequences. This investigation examined the self-treatment behaviors in patients with diabetes mellitus in Iran/kashan.</p> <p>Methods</p> <p>The patients who referred to the clinic of diabetes and those who were admitted to the General hospital in the city of Kashan due to diabetes mellitus were asked to participate in this cross-sectional study. For data collection, The 25 item questionnaire of Likert scale type with four scales was used. Factor analysis was performed to define the patterns of self-treatment.</p> <p>Results</p> <p>398 patients participated in the study. The mean age of the study population was 54.9 ± 12.9 years. The majority (97%) had type 2 diabetes. 50% of patients reported self- treatment. The self-treatment score was 45.8 ± 8.8 (25-100). Female gender, lower education and co-morbid illnesses of hypertension, hyperlipidemia and cardiac disease had significant relationship with self-treatment. The factor analysis procedure revealed seven factors that explained the 43% of variation in the self-treatment. These seven factors were categorized as knowledge, deficiencies of formal treatments, available self-treatment methods, physician related factors, the tendency to use herbal remedies, underlying factors such as gender and factors related to diabetes.</p> <p>Conclusions</p> <p>There is a medium tendency for self-treatment in diabetic patients. The assessment of self-treatment practices must be an essential part of patients' management in diabetes care.</p

The role of chemotherapeutic drugs in the evaluation of breast tumour response to chemotherapy using serial FDG-PET

INTRODUCTION: The aims of this study were to investigate whether drug sequence (docetaxel followed by anthracyclines or the drugs in reverse order) affects changes in the maximal standard uptake volume (SUVmax) on [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) during neoadjuvant chemotherapy in women with locally advanced breast cancer. METHODS: Women were randomly assigned to receive either drug sequence, and FDG-PET scans were taken at baseline, after four cycles and after eight cycles of chemotherapy. Tumour response to chemotherapy was evaluated based on histology from a surgical specimen collected upon completion of chemotherapy. RESULTS: Sixty women were enrolled into the study. Thirty-one received docetaxel followed by anthracyclines (Arm A) and 29 received drugs in the reverse order (Arm B). Most women (83%) had ductal carcinoma and 10 women (17%) had lobular or lobular/ductal carcinoma. All but one tumour were downstaged during therapy. Overall, there was no significant difference in response between the two drug regimens. However, women in Arm B who achieved complete pathological response had mean FDG-PET SUVmax reduction of 87.7% after four cycles, in contrast to those who had no or minor pathological response. These women recorded mean SUVmax reductions of only 27% (P < 0.01). Women in Arm A showed no significant difference in SUVmax response according to pathological response. Sensitivity, specificity, accuracy and positive and negative predictive values were highest in women in Arm B. CONCLUSIONS: Our results show that SUVmax uptake by breast tumours during chemotherapy can be dependent on the drugs used. Care must be taken when interpreting FDG-PET in settings where patients receive varied drug protocols

Untangling knowledge creation and knowledge integration in enterprise wikis

A central challenge organizations face is how to build, store, and maintain knowledge over time. Enterprise wikis are community-based knowledge systems situated in an organizational context. These systems have the potential to play an important role in managing knowledge within organizations, but the motivating factors that drive individuals to contribute their knowledge to these systems is not very well understood. We theorize that enterprise wiki initiatives require two separate and distinct types of knowledge-sharing behaviors to succeed: knowledge creation (KC) and knowledge integration (KI). We examine a Wiki initiative at a major German bank to untangle the motivating factors behind KC and KI. Our results suggest KC and KI are indeed two distinct behaviors, reconcile inconsistent findings from past studies on the role of motivational factors for knowledge sharing to establish shared electronic knowledge resources in organizations, and identify factors that can be leveraged to tilt behaviors in favor of KC or KI

Knowledge Management and Organisational Culture

This international Handbook provides a comprehensive overview of key topics, debates and issues within the now well-established field of Knowledge Management (KM)

Bi-allelic loss-of-function variants in PPFIBP1 cause a neurodevelopmental disorder with microcephaly, epilepsy, and periventricular calcifications

PPFIBP1 encodes for the liprin-β1 protein, which has been shown to play a role in neuronal outgrowth and synapse formation in Drosophila melanogaster. By exome and genome sequencing, we detected nine ultra-rare homozygous loss-of-function variants in 16 individuals from 12 unrelated families. The individuals presented with moderate to profound developmental delay, often refractory early-onset epilepsy, and progressive microcephaly. Further common clinical findings included muscular hyper- and hypotonia, spasticity, failure to thrive and short stature, feeding difficulties, impaired vision, and congenital heart defects. Neuroimaging revealed abnormalities of brain morphology with leukoencephalopathy, ventriculomegaly, cortical abnormalities, and intracranial periventricular calcifications as major features. In a fetus with intracranial calcifications, we identified a rare homozygous missense variant that by structural analysis was predicted to disturb the topology of the SAM domain region that is essential for protein-protein interaction. For further insight into the effects of PPFIBP1 loss of function, we performed automated behavioral phenotyping of a Caenorhabditis elegans PPFIBP1/hlb-1 knockout model, which revealed defects in spontaneous and light-induced behavior and confirmed resistance to the acetylcholinesterase inhibitor aldicarb, suggesting a defect in the neuronal presynaptic zone. In conclusion, we establish bi-allelic loss-of-function variants in PPFIBP1 as a cause of an autosomal recessive severe neurodevelopmental disorder with early-onset epilepsy, microcephaly, and periventricular calcifications