Visual Exploration of Financial Data with Incremental Domain Knowledge.

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.

Model-driven engineering city spaces via bidirectional model transformations.

Engineering cyber-physical systems inhabiting contemporary urban spatial environments demands software engineering facilities to support design and operation. Tools and approaches in civil engineering and architectural informatics produce artifacts that are geometrical or geographical representations describing physical spaces. The models we consider conform to the CityGML standard; although relying on international standards and accessible in machine-readable formats, such physical space descriptions often lack semantic information that can be used to support analyses. In our context, analysis as commonly understood in software engineering refers to reasoning on properties of an abstracted model-in this case a city design. We support model-based development, firstly by providing a way to derive analyzable models from CityGML descriptions, and secondly, we ensure that changes performed are propagated correctly. Essentially, a digital twin of a city is kept synchronized, in both directions, with the information from the actual city. Specifically, our formal programming technique and accompanying technical framework assure that relevant information added, or changes applied to the domain (resp. analyzable) model are reflected back in the analyzable (resp. domain) model automatically and coherently. The technique developed is rooted in the theory of bidirectional transformations, which guarantees that synchronization between models is consistent and well behaved. Produced models can bootstrap graph-theoretic, spatial or dynamic analyses. We demonstrate that bidirectional transformations can be achieved in practice on real city models.

Gait kinematics in Low Back Pain: A non-linear approach.

BACKGROUND: Kinematic analysis has been a dominant tool for addressing the neuromuscular and proprioceptive alterations that occur in Low Back Pain (LBP) patients. Movement variability is a crucial component of this analysis. In the recent years application of non-linear indices seems to be showing the way. OBJECTIVE: The aim of the study was to compare movement variability, as expressed mainly by non-linear indices, at the pelvis and lumbar spine between LBP patients and healthy participants during gait. METHODS: Sixteen (16) LBP patients and thirteen (13) healthy control subjects (non-athletes) participated in the study. Participants walked on a treadmill at different walking conditions while recorded by a 6-infrared camera optoelectronic system. Kinematic variability of pelvic and lumbar movement was analyzed using linear (standard deviation - SD) and non-linear indices (Maximal Lyapunov Exponent - LyE and Approximate Entropy - ApEn). RESULTS: Healthy subjects were found to have significantly greater mean values than LBP patients at seven pelvic and lumbar components in LyE, ApEn and SD. Specifically, the calculated LyE at the pelvis during normal gait was proven to have a sensitivity of 92.3% and a specificity of 90% in the discrimination of healthy subjects from LBP patients. Female subjects presented with higher variability in gait measures than males. CONCLUSION: Healthy participants presented with higher movement variability in their kinematic behavior in comparison to LBP patients. Lower variability values may be partly explained by the attempt of LBP patients to avoid painful end of range of motion positions. In this perspective non-linear indices seem to relate to qualitive characteristics of movement that need to be taken into consideration during rehabilitation.

Static and dynamic balance deficiencies in chronic low back pain.

BACKGROUND: According to previously conducted studies, people with Low Back Pain (LBP) present with static balance deficiencies. OBJECTIVE: The aim of the present study was to compare static, as well as dynamic balance ability between Chronic Low Back Pain (CLBP) and healthy subjects. METHODS: The CLBP group comprised 17 subjects and the control group of 16 subjects, matched for age, BMI and gender. The protocol applied compared the balance ability when performing the Star Excursion Balance Test (SEBT) and the static 1-leg stance position. The innovation introduced in the protocol was that the participants performed not only the static 1-leg stance, but also the dynamic SEBT on a force plate which recorded the target sway (TS), i.e. the Center of Pressure (CoP) excursion. RESULTS: The CLBP group had significantly reduced performance in SEBT, coupled with greater static and dynamic TS values. Age and especially BMI also bear a significant effect on SEBT execution. The inclusion of SEBT and TS derived scores in a stepwise logistic regression equation lead to the correct classification of 85% of the subjects. CONCLUSIONS: Dynamic and static balance ability provide supplementary information for the identification of the presence of CLBP, with dynamic balance being more instrumental.