Transport of magnetic flux and mass in Saturn's inner magnetosphere

It is well accepted that cold plasma sourced by Enceladus is ultimately lost to the solar wind, while the magnetic flux convecting outward with the plasma must return to the inner magnetosphere. However, whether the interchange or reconnection, or a combination of the two processes is the dominant mechanism in returning the magnetic flux is still under debate. Initial Cassini observations have shown that the magnetic flux returns in the form of flux tubes in the inner magnetosphere. Here we investigate those events with 10 year Cassini magnetometer data and confirm that their magnetic signatures are determined by the background plasma environments: inside (outside) the plasma disk, the returning magnetic field is enhanced (depressed) in strength. The distribution, temporal variation, shape, and transportation rate of the flux tubes are also characterized. The flux tubes break into smaller ones as they convect in. The shape of their cross section is closer to circular than fingerlike as produced in the simulations based on the interchange mechanism. In addition, no sudden changes in any flux tube properties can be found at the “boundary” which has been claimed to separate the reconnection and interchange-dominant regions. On the other hand, reasonable cold plasma loss rate and outflow velocity can be obtained if the transport rate of the magnetic flux matches the reconnection rate, which supports reconnection alone as the dominant mechanism in unloading the cold plasma from the inner magnetosphere and returning the magnetic flux from the tail

Effective fair pricing of international mutual funds

We propose a new methodology to provide fair prices of international mutual funds by adjusting prices at the individual security level using a comprehensive and economically relevant information set. Stepwise regressions are used to endogenously determine the stock-specific optimal set of factors. Using 16 synthetic funds whose characteristics are extracted from 16 corresponding actual US-based Japanese mutual funds, we demonstrate that our method estimates fund prices significantly more accurately than existing methods. Although existing fair-pricing methods provide an improvement over the current practice of simply using Japanese market closing prices, they are still highly vulnerable to exploitation by market-timers. By contrast, our method is the most successful in preventing such strategic exploitation since no competing method can profit from our stated prices. © 2008 Elsevier B.V. All rights reserved.preprin

The impact of environmental and human factors on urban heat and microclimate variability

Urbanization is known to cause noticeable changes in the properties of local climate. Studies have shown that urban areas, compared to rural areas with less artificial surfaces, register higher local temperatures as a result of Urban Heat Islands (UHIs). Hong Kong is one of the most densely populated cities in the world and a high proportion of its population residing in densely built high-rise buildings are experiencing some degrees of thermal discomfort. This study selected Mong Kok and Causeway Bay, two typical urban communities in Hong Kong, to gather evidence of microclimate variation and sources of thermal discomfort. UHIs were estimated from 58 logging sensors placed at strategic locations to take temperature and humidity measurements over 17 consecutive days each in the summer/hot and winter/cool periods. By employing geographic information and global positioning systems, these measurements were geocoded and plotted over the built landscape to convey microclimate variation. The empirical data were further aligned with distinct environmental settings to associate possible factors contributing to UHIs. This study established the existence and extent of microclimate variation of UHI within urban communities of different environmental configuration and functional uses. The findings provided essential groundwork for further studies of UHI effects to inform sources of local thermal discomfort and better planning design to safeguard environmental health in public areas.postprin

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

Classification of migraine stages based on resting-state EEG power

© 2015 IEEE. Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs' resting-state EEG power. We collect migraineurs' O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time

Working memory impairment and its associated sleep-related respiratory parameters in children with obstructive sleep apnea

Study Objective: Working memory deficits in children with obstructive sleep apnea (OSA) have been reported in previous studies, but the results were inconclusive. This study tried to address this issue by delineating working memory functions into executive processes and storage/maintenance components based on Baddeley’s working memory model. Methods: Working memory and basic attention tasks were administered on 23 OSA children aged 8–12 years and 22 age-, education-, and general cognitive functioning-matched controls. Data on overnight polysomnographic sleep study and working memory functions were compared between the two groups. Associations between respiratory-related parameters and cognitive performance were explored in the OSA group. Results: Compared with controls, children with OSA had poorer performance on both tasks of basic storage and central executive components in the verbal domain of working memory, above and beyond basic attention and processing speed impairments; such differences were not significant in the visuo-spatial domain. Moreover, correlational analyses and hierarchical regression analyses further suggested that obstructive apnea–hypopnea index (OAHI) and oxygen saturation (SpO2) nadir were associated with verbal working memory performance, highlighting the potential pathophysiological mechanisms of OSA induced cognitive deficits. Conclusions: Verbal working memory impairments associated with OSA may compromise children’s learning potentials and neurocognitive development. Early identification of OSA and assessment of the associated neurocognitive deficits are of paramount importance. Reversibility of cognitive deficits after treatment would be a critical outcome indicatorpostprin

How can the R.E.N.A.L. nephrometry scoring system aid management of a solid renal mass?

OBJECTIVES. To investigate use of the R.E.N.A.L. nephrometry score in relation to the choice of treatment and postoperative complications for renal masses. DESIGN. Case series. SETTING. A tertiary referral hospital in Hong Kong. PATIENTS. Data of patients undergoing nephrectomy were collected retrospectively from a clinical database and analysed. A R.E.N.A.L. nephrometry score was allocated to each renal tumour by a blinded qualified radiologist, utilising computerised imaging systems. Patient demographics, choice of surgery (radical vs partial), and approaches (open vs minimally invasive) were analysed with respect to their R.E.N.A.L. score. RESULTS. In all, 74 patients were included during the study period, of which 38 underwent partial nephrectomy and 36 underwent radical nephrectomy. No differences between the groups were found with respect to patient demographics. There were significant differences between the partial and radical nephrectomy groups in terms of their mean nephrometry score (6.9 vs 9.3, P<0.001). The mean nephrometry sum was also significantly different in the open approach versus the minimally invasive approach in patients having partial nephrectomy (7.8 vs 6.0, P=0.001). There was no difference in the postoperative 90-day morbidity and mortality in the partial nephrectomy and radical nephrectomy groups. CONCLUSIONS. The R.E.N.A.L. nephrometry score of a renal mass correlated significantly with our choice of surgery (partial vs radical) and our approach to surgery (open vs minimally invasive surgery), particularly in the partial nephrectomy group. It does not, however, correlate with postoperative complications. The nephrometry score provides a useful tool for objectively describing renal mass characteristics and enhancing better communication for the operative planning directed at renal masses.published_or_final_versio

Resting-state EEG power and coherence vary between migraine phases

© 2016, The Author(s). Background: Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases. Methods: We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence of delta (1–3.5 Hz), theta (4–7.5 Hz), alpha (8–12.5 Hz), and beta (13–30 Hz) bands were calculated in the frontal, central, temporal, parietal, and occipital regions. Results: Fifty patients with episodic migraine (1–5 headache days/month) and 20 HCs completed the study. Patients were classified into inter-ictal, pre-ictal, ictal, and post-ictal phases (n = 22, 12, 8, 8, respectively), using 36-h criteria. Compared to HCs, inter-ictal and ictal patients, but not pre- or post-ictal patients, had lower EEG power and coherence, except for a higher effective connectivity in fronto-occipital network in inter-ictal patients (p <.05). Compared to data obtained from the inter-ictal group, EEG power and coherence were increased in the pre-ictal group, with the exception of a lower effective connectivity in fronto-occipital network (p <.05). Inter-ictal and ictal patients had decreased EEG power and coherence relative to HCs, which were “normalized” in the pre-ictal or post-ictal groups. Conclusion: Resting-state EEG power density and effective connectivity differ between migraine phases and provide an insight into the complex neurophysiology of migraine

Association between Androgen Deprivation Therapy and Risk of Dementia in Men with Prostate Cancer

The risk of dementia after androgen deprivation therapy (ADT) in patients with advanced prostate cancer (PCa) remains controversial. This study aimed to evaluate the association between ADT and the incidence of dementia in patients with PCa. We identified patients newly diagnosed with PCa in the National Health Insurance Database of Taiwan from 1 January 2002 to 30 June 2016 and in The Health Improvement Network of the United Kingdom (UK) from 1 January 1998 to 31 March 2018. We classified patients with PCa into ADT and ADT-naïve groups. Propensity score (PS) methods were used to minimize the differences in characteristics between the groups. We performed a Cox proportional hazard model to obtain the adjusted hazard ratio (HR) to compare the incidence of dementia between the groups. Our ADT group comprised 8743 and 73,816 patients in Taiwan and the UK, respectively, which were matched 1:1 to ADT-naïve patients by PS. The incidence rates of dementia in the ADT group were 2.74 versus 3.03 per 1000 person-years in the ADT naïve groups in Taiwan, and 2.81 versus 2.79 per 1000 person-years in the UK. There was no statistical difference between ADT and ADT-naïve groups (adjusted HR: 1.12; 95% confidence interval (CI): 0.87–1.43 in Taiwan and adjusted HR: 1.02; 95% CI: 0.85–1.23 in the UK). We found no association between the incidence of dementia and ADT in patients with advanced PCa in either database. Further studies are warranted to evaluate other possible triggers of incident dementia in patients receiving ADT for advanced PCa

Single channel wireless EEG device for real-time fatigue level detection

© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments