BACKGROUND MS symptoms affect many functional domains. Knowing the specific impact of symptoms on health-related quality of life (HRQoL) is vital for successful disease and symptom management in MS. We aimed at investigating how specific MS symptoms contribute to the disease burden in individuals and from a population perspective. METHODS We included 855 Swiss Multiple Sclerosis Registry participants with a relapsing-remitting form (RRMS) or a progressive form (PMS). HRQoL was measured with the EuroQol 5-Dimension EQ-5D-index and EQ-Visual Analogue Scale (EQ-VAS) on 0-100% scales. Their associations with 20 symptoms, socio-demographic and clinical information were explored in median regression models, stratified by RRMS and PMS. RESULTS We included 611 participants with RRMS and 244 with PMS. In RRMS, gait (-6.5%) and balance problems (-5.1%) had the largest EQ-5D-index reductions, and were also important at the population level (frequencies 45% and 52%). Fatigue, depression, and spasticity (frequencies 74.1%, 31%, 38%) also contributed to the population disease burden. In PMS, spasticity, paralysis, and bowel problems had the largest impact on EQ-5D-index, both at the individual and population levels. The largest impact on EQ-VAS at population level was associated in RRMS with balance problems, depression, dizziness, and spasticity, while in PMS with weakness, pain, and paralysis. CONCLUSIONS While HRQoL at population level is most affected by balance problems, spasticity, and depression in RRMS, the biggest HRQoL losses in PMS are caused by spasticity, paralysis, weakness, and pain. Many symptoms with the largest effects in individuals substantially contribute to the population disease burden.

—We consider the improvement of dynamic security in power systems by tuning of the power plant controllers. The parameters of existing controllers are tuned like power system stabilizers in order to increase the stability reserve and oscillation damping after a dropout of power plants and power lines. The tuning is done in two steps: in the first step, the power system is stabilized after the dropout of a power plant or power line. Then, oscillation damping is increased after the dropout by an H-infinity optimization approach. Both steps use linear matrix inequality optimization methods. To perform the tuning, we introduce a modeling method for the dropout of power plants and power lines, such that the dropout of components can be considered directly as a disturbance input for the H-infinity optimization. Finally, we evaluate the approach on the IEEE 39 bus model. We show that the presented methods successfully stabilize the system and improve power oscillation damping after the dropout of different power plants and power lines.

Abstract Background We applied machine learning to find a novel breast cancer predictor based on information in a mammogram. Methods Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index. Results Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10−6). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10−5). Conclusions A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.

Pushing is a common task in robotic scenarios. In real-world environments, robots need to manipulate various unknown objects without previous experience. We propose a data-driven approach for learning local inverse models of robot-object interaction for push manipulation. The robot makes observations of the object behaviour on the fly and adapts its movement direction. The proposed model is probabilistic, and we update it using maximum a posteriori (MAP) estimation. We test our method by pushing objects with a holonomic mobile robot base. Validation of results over a diverse object set demonstrates a high degree of robustness and a high success rate in pushing objects towards a fixed target and along a path compared to previous methods. Moreover, based on learned inverse models, the robot can learn object properties and distinguish between different object behaviours when they are pushed from different sides.

Introduction: Population aging increases the number of people with dementia. Dementia is a set of symptoms that include memory difficulties, learning difficulties, speech and language difficulties, disorientation in time and space, difficulties in understanding and behavioral changes. Dementia is not part of natural aging and needs to be understood as such and have to be recognized at time to provide adequate support for people with dementia. Aim: To present the importance of communication: To present communication difficulties which are the result of dementia; To present adaptations in the way of communicating with people with dementia. Material and methods: The article has a descriptive character, and represents a review of the literature dealing with this topic. Results: Difficulties in area of language are a common symptom in people with dementia. Those communication difficulties are a consequence of nerve cell failure, and person with dementia should not be blamed of the symptoms that arise. People with dementia show lower results in the area of understanding and verbal expression, repetition, reading and writing. Syntax and phonology remain relatively intact in early stages, but semantic abilities are impaired. Conclusion: Communication for people with dementia and with people with dementia for all persons involved in care (including family members, medical staff and therapists, and members of the community) can be very challenging. It is often necessary to adapt the way of communication to avoid stress and negative feelings in a person with dementia. As the disease causing dementia progresses, communication problems are increasing as well. Many times caregivers and therapists are in situations where their communicative behavior (verbal, but also nonverbal) needs to show support, compassion, care, and desire to help.

Multi-, many-core, hybrid processors and parallel programming languages are slowly becoming pervasive in main- stream computing. It is expected that they will affect a large spectrum of systems, from embedded and general-purpose, to high-end computing systems. This architectural change has already challenged programmers to efficiently write an application code that can scale over many cores to utilize its computational power. Moreover, many heterogeneous architectures exist today, hence there was an emergent need for a uniform interface to these architectures. Recently, Khronos Group defined the Open Computing Language (OpenCL) for abstracting the underlying hardware, which enables software developers to write a portable code across different shared-memory architectures. In this paper, we introduce a new parallel implementation of one of the fastest image segmentation algorithms known as Simple Linear Iterative Clustering based on OpenCL. We evaluate the effectiveness of this implementation using only multi-core GPCPU. Our implementation is fully compatible with sequential implementation. When the algorithm is executed sequentially it utilizes only 25% of total computational power of a GPCPU for any image resolution, while its modified algorithm is able to utilize close to 100% for high resolution images. The resulting algorithm is up to 5×faster than its sequential counterpart.

Driven by higher energy demand, the complications of finding new corridors, construction, and slow commissioning procedures for transmission lines, an advanced technology has been innovated to adapt to the modern power system's needs. A new type of overhead conductor with a polymer composite core (ACCC) have a greater ampacity, lower sag, and are lighter than traditional ACSR conductors. Therefore, it is this innovative technology is optimal choice when the decommissioning of old lines takes place. This paper analyzes the impact of the gradual replacement of old transmission lines with all the benefits and disadvantages. It addresses the issues of equipment aging and proposes a replacement strategy for a cost-effective transition to a modernized power system functioning at improved performance. Calculations and analyses are performed on a real interconnected system, composed of several South East European (SEE) transmission grids.

For interconnected and complex systems, security is paramount for establishing trust in their correctness and design adequacy. Thus, security needs to be assured and a corresponding security assurance case needs to be presented to system stakeholders, security assessors, as well as to system users. However, security is dynamic by its nature and to maintain its acceptable security level, frequent updates might be required. Traditionally, a security assurance case is built from scratch whenever a change occurs, however given the cost of resources needed for such a task, a more effective and less time consuming way of handling updates is needed. Hence, the challenge of security case run-time adaptation is considered in this work. We survey the state of the art in security assurance and security case development to refine the challenge and identify system decomposition as one the enablers for security case run-time adaptation. We propose to apply system decomposition in terms of services and use service choreographies to facilitate security case run-time adaptation. The proposed approach is illustrated on an E-gas example.