Quantum Fluctuation Theorems

Recent advances in experimental techniques allow one to measure and control systems at the level of single molecules and atoms. Here gaining information about fluctuating thermodynamic quantities is crucial for understanding nonequilibrium thermodynamic behavior of small systems. To achieve this aim, stochastic thermodynamics offers a theoretical framework, and nonequilibrium equalities such as Jarzynski equality and fluctuation theorems provide key information about the fluctuating thermodynamic quantities. We review the recent progress in quantum fluctuation theorems, including the studies of Maxwell's demon which plays a crucial role in connecting thermodynamics with information.Comment: As a chapter of: F. Binder, L. A. Correa, C. Gogolin, J. Anders, and G. Adesso (eds.), "Thermodynamics in the quantum regime - Fundamental Aspects and New Directions", (Springer International Publishing, 2018

A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically signifcant using z-test with p value <0.0001

The links between health-related behaviors and life satisfaction in elderly individuals who prefer institutional living

BACKGROUND: Life satisfaction among residents of institutions is becoming an important issue in a rapidly aging population. The aim of this cross-sectional study was to investigate the links between life satisfaction and health-related behaviors amongst functionally independent elderly people who prefer institutional living in İstanbul, Turkey. METHODS: The socio-demographic characteristics, health-related behaviors, leisure-time activities and fall histories of 133 residents of an institution in Istanbul were assessed by a structured questionnaire during face-to-face interviews. A validated life-satisfaction index questionnaire (LSI-A) was completed. RESULTS: The mean age of the study group was 73.9 ± 8.0 (range 60–90 years). Within the group, 22.6% had never married and 14.3% had university degrees. The majority (71.4%) were in the low income bracket. The overall mean LSI-A score was 20.3 ± 5.9. Participants who declared moderate/high income levels had a significantly higher mean LSI-A score than those in the low-income bracket (p = 0.009). Multivariate analysis of the data suggested that leisure-time activities and participation in regular physical activities are significant predictors of LSI-A scores (R(2): 0.112; p = 0.005 and p = 0.02, respectively). CONCLUSION: The findings imply that regular physical activity and leisure-time activities are significantly related to life satisfaction among residents in institutions. Participation in physical activity and leisure-time activity programs may help to improve the life satisfaction of elderly people living in institutions

External versus internal fixation for bicondylar tibial plateau fractures: systematic review and meta-analysis.

BACKGROUND: It is uncertain whether external fixation or open reduction internal fixation (ORIF) is optimal for patients with bicondylar tibial plateau fractures. MATERIALS AND METHODS: A systematic review using Ovid MEDLINE, Embase Classic, Embase, AMED, the Cochrane Library, Open Grey, Orthopaedic Proceedings, WHO International Clinical Trials Registry Platform, Current Controlled Trials, US National Institute for Health Trials Registry, and the Cochrane Central Register of Controlled Trials. The search was conducted on 3rd October 2014 and no language limits were applied. Inclusion criteria were all clinical study designs comparing external fixation with open reduction internal fixation of bicondylar tibial plateau fractures. Studies of only one treatment modality were excluded, as were those that included unicondylar tibial plateau fractures. Treatment effects from studies reporting dichotomous outcomes were summarised using odds ratios. Continuous outcomes were converted to standardized mean differences to assess the treatment effect, and inverse variance methods used to combine data. A fixed effect model was used for meta-analyses. RESULTS: Patients undergoing external fixation were more likely to have returned to preinjury activities by six and twelve months (P = 0.030) but not at 24 months follow-up. However, external fixation was complicated by a greater number of infections (OR 2.59, 95 % CI 1.25-5.36, P = 0.01). There were no statistically significant differences in the rates of deep infection, venous thromboembolism, compartment syndrome, or need for re-operation between the two groups. CONCLUSION: Although external fixation and ORIF are associated with different complication profiles, both are acceptable strategies for managing bicondylar tibial plateau fractures

Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity

Scaling Effects and Spatio-Temporal Multilevel Dynamics in Epileptic Seizures

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures