Background: The natural fluctuation of motor symptoms of Parkinson's disease ( PD ) makes judgement of any change challenging and the use of clinical scales such as the International Parkinson and Movement Disorder Society ( MDS )‐ UPDRS imperative. Recently developed commodity mobile communication devices, such as smartphones, could possibly be used to assess motor symptoms in PD patients in a convenient way with low cost. We provide the first report on the development and testing of stand‐alone software for mobile devices that could be used to assess both tremor and bradykinesia of PD patients. Methods: We assessed motor symptoms with a custom‐made smartphone application in 14 patients and compared the results with their MDS ‐ UPDRS scores. Results: We found significant correlation between five subscores of MDS ‐ UPDRS (rest tremor, postural tremor, pronation‐supination, leg agility, and finger tapping) and eight parameters of the data collected with the smartphone. Conclusions: These results provide evidence as a proof of principle that smartphones could be a useful tool to objectively assess motor symptoms in PD in clinical and experimental settings.

External representations have been shown to play a key role in mediating cognition. Tangible environments offer the opportunity for novel representational formats and combinations, potentially increasing representational power for supporting learning. However, we currently know little about the specific learning benefits of tangible environments, and have no established framework within which to analyse the ways that external representations work in tangible environments to support learning. Taking external representation as the central focus, this paper proposes a framework for investigating the effect of tangible technologies on interaction and cognition. Key artefact-action-representation relationships are identified, and classified to form a structure for investigating the differential cognitive effects of these features. An example scenario from our current research is presented to illustrate how the framework can be used as a method for investigating the effectiveness of differential designs for supporting science learning.

The performance of wireless sensor networks (WSN) is prone to adverse influences from a number of factors such as the interference from co-located wireless systems utilising the same spectral space. Channel hopping technique was proposed to mitigate the problem via periodic change of the operating frequency, and has been adopted in the form of time slotted channel hopping (TSCH) by IEEE 802.15.4e standard. This paper proposes adaptive slotted channel hopping (A-TSCH), an enhanced version of the TSCH aided by blacklisting technique. Complete design and implementation specifics are provided; and the results of experiments are analysed to show its advantages over existing TSCH. The main finding of this work is that A-TSCH can significantly improve the reliability of channel hopping scheme and thus provide better protection from interference for wireless sensor networks.

Modern artificial intelligence is inherently paradoxical in many ways. While AI aims to increase automation, it also requires more intimate human involvement to reflect on the insights generated (automation paradox). While AI results in job displacement, it also creates new jobs, some simply to provide the necessary support systems for those newly unemployed (transition paradox). And as generative AI takes away control over the creative process, it also offers new creative opportunities (creativity paradox). This article considers another paradox, that relates to the fact that computational systems created using AI can be used both for public good in civilian applications and for harm across a range of application areas and settings [A1]. This contradiction is explored within an organizational and governmental context, where modern AI relies on data which might be externally or internally-sourced [A2]. External data sources [A3] are inclusive of open-source intelligence (OS-INT), such as information available on the Internet and the dark web, and internal data sources may include proprietary data found within an organizational or a wider governmental context [A4]. A further relevant consideration is the expanding role of the Internet of Things to support smart infrastructures, which has created new vulnerabilities [A5].