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Based on an analysis of the New Zealand data in the Work Orientation module of the International Social Survey Programme (ISSP) across three rounds (1997, 2005 and 2015), this paper examines how workers in New Zealand perceive their job quality. These surveys imply that New Zealanders have relatively good jobs, as shown in healthy levels of job quality and job satisfaction. They rate highly the quality of their collegial relationships at work and typically perceive the intrinsic quality of their job as better than the extrinsic quality. A key issue in relation to the latter is that they generally do not rate their advancement opportunities as high. While men, full-timers and graduates have some advantages over women, part-timers and non-graduates in extrinsic job quality, the intrinsic quality of work is more evenly experienced. In terms of intrinsic issues, the rising level of stress from 2005 to 2015 poses a concern and there is no evidence that graduates enjoy any kind of premium in the intrinsic quality of work apart from a lower level of hard physical effort.

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children’s Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.

In some steep valleys, flood-induced changes in river bed elevation pose significantly greater risks to infrastructure than floodwaters alone. Over the short term, the river may aggrade or degrade by several meters during a single flood. Whereas floodwaters recede after each event, moreover, riverbed changes add up over successive floods. To quantify the resulting geomorphic risk and its evolution over time, we propose in this paper a new stochastic model of river bed elevation change. The bed is assumed to rise and drop according to a random walk, driven by the composition of two gamma processes that respectively pace the hydrologic forcing and the geomorphic response. The model can therefore incorporate various sources of uncertainty, associated with precipitation and debris flow activity within the contributing watershed. To test the model, we apply it to a highly active montane river, the Laonong River in southwestern Taiwan. Model calibration is achieved from a combination of long and short term data, including radiocarbon-dated deposits and modern river records. The modelled distributions fit the data well, including the likelihood of extreme changes. The model also produces a reasonable hindcast of the geomorphic damage suffered over the last ten years by Highway 20, a vulnerable road link sited along the river, and can be used to forecast future geomorphic risk., Structural Design & Mechanics

Electrocardiography plays an essential role in diagnosing and screening cardiovascular diseases in daily healthcare. Deep neural networks have shown the potentials to improve the accuracies of arrhythmia detection based on electrocardiograms (ECGs). However, more ECG records with ground truth are needed to promote the development and progression of deep learning techniques in automatic ECG analysis. Here we propose a web-based tool for ECG viewing and annotating, LabelECG. With the facilitation of unified data management, LabelECG is able to distribute large cohorts of ECGs to dozens of technicians and physicians, who can simultaneously make annotations through web-browsers on PCs, tablets and cell phones. Along with the doctors from four hospitals in China, we applied LabelECG to support the annotations of about 15,000 12-lead resting ECG records in three months. These annotated ECGs have successfully supported the First China ECG intelligent Competition. La-belECG will be freely accessible on the Internet to support similar researches, and will also be upgraded through future works.

Sha Zhang,1,* Long Meng,1,* Feng Qiu,1 Jia-Dan Yang,1 Shusen Sun2 1Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Pharmacy Practice, College of Pharmacy and Health Sciences, Western New England University, Springfield, MA, USA *These authors contributed equally to this work Background: Previous studies have demonstrated that medication adherence has an impact on health-related quality of life (HRQoL). However, other medication-related factors that may influence HRQoL have not been extensively studied, especially factors based on the Medication-Risk Questionnaire (MRQ), and such studies are mostly done in Western countries. Our objective was to explore risk factors associated with HRQoL among community-dwelling elderly with chronic diseases in mainland China, especially the medication-related risk factors regarding MRQ.Methods: The study was conducted in a community health service center through surveys to eligible patients. The main outcomes of HRQoL were assessed by the EuroQol-5D (EQ-5D) scale and EQ-visual analog scale (EQ-VAS). Medication-related risk factors according to MRQ associated with HRQoL were identified using a multiple linear regression.Results: A total of 311 patients were analyzed, averaging 71.19±5.33 years, and 68.8% were female. The mean EQ-5D index was 0.72±0.09, and the mean EQ-VAS score was 71.37±11.97. The most prevalent problem was pain/discomfort, and 90.0% believed that they could take care of themselves without any problems. Sex, age, educational level, frailty, function status, and certain medication-related factors regarding MRQ were found to be significant factors impacting the HRQoL. A multivariate analysis showed that MRQ factors of polypharmacy, multimorbidity, feeling difficultly with taking medicines as prescribed, and taking medicines with narrow therapeutic index had negative impacts o

The erosion of mountain belts controls their topographic and structural evolution1, 2, 3 and is the main source of sediment delivered to the oceans4. Mountain erosion rates have been estimated from current relief and precipitation, but a more complete evaluation of the controls on erosion rates requires detailed measurements across a range of timescales. Here we report erosion rates in the Taiwan mountains estimated from modern river sediment loads, Holocene river incision and thermochronometry on a million-year scale. Estimated erosion rates within the actively deforming mountains are high (3–6 mm yr-1) on all timescales, but the pattern of erosion has changed over time in response to the migration of localized tectonic deformation. Modern, decadal-scale erosion rates correlate with historical seismicity and storm-driven runoff variability. The highest erosion rates are found where rapid deformation, high storm frequency and weak substrates coincide, despite low topographic relief.