Enabling IoT ecosystems through platform interoperability

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.Peer ReviewedPostprint (author's final draft

Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig

Residential choice behaviour is a complex process underpinned by both housing market restrictions and individual preferences, which are partly conscious and partly tacit knowledge. Due to several limitations, common survey methods cannot sufficiently tap into such tacit knowledge. Thus, this paper introduces an advanced knowledge elicitation process called SilverKnETs and combines it with data mining using random forests to elicit and operationalize this type of knowledge. For the application case of the city of Leipzig, Germany, our findings indicate that rent, location and type of housing form the three predictors strongly influencing the decision making in residential choices. Other explanatory variables appear to have a much lower influence. Random forests have proven to be a promising tool for the prediction of residential choices, although the design and scope of the study govern the explanatory power of these models

Overview and Comparison of Asset Information Model Standards

Different organizations are currently working on concepts and standards pertaining to the integration of industrial automation devices into a communication network. For manufacturers, suppliers, integrators, and operators of automation components, the variety of available protocols for information exchange raises the question of which standard to use. To address this question, this contribution provides an overview of different standards for the virtual description of an automation device in the context of device integration and presents a detailed comparison of the following selected standards: W3C WoT Thing Description, Asset Administration Shell, Digital Factory Framework, Automation Markup Language, Module Type Package, OPC UA Process Automation - Device Information Model, and Field Device Integration. These standards are compared with respect to four categories: 1) Representation of a property; 2) Representation of services; 3) Information modeling for direct automation device access; and 4) Mechanism for discovery. The comparison is summarized in an evaluation of the suitability of each standard for different use cases. Since none of the standards fully covers all use cases generic integration strategies are presented for combining the device information models. Finally, a description of a demonstration showcasing this integration, including an implementation as a proof of concept, concludes this contribution

Equal access to outreach mental health care? Exploring how the place of residence influences the use of intensive home treatment in a rural catchment area in Germany

Abstract Background Internationally, intensive psychiatric home treatment has been increasingly implemented as a community-based alternative to inpatient admission. Since 2018, the so-called Inpatient Equivalent Home Treatment (IEHT; German: "Stationsäquivalente Behandlung", short: "StäB") has been introduced as a particularly intensive form of home treatment that provides at least one daily treatment contact in the service users’ (SU) home environment. Prior research shows that this can be challenging in rural catchment areas. Our paper investigates to which extent the location of the SU home location within the catchment area as well as the distance between the home and the clinic influence the utilisation of inpatient treatment compared to IEHT. Method Routine data of one psychiatric hospital in the federal state of Brandenburg in Germany were analysed for the observational period 07/2018–06/2021. Two comparison groups were formed: SU receiving inpatient treatment and SU receiving IEHT. The SU places of residence were respectively anonymised and converted into geo-coordinates. A geographic information system (GIS) was used to visualise the places of residence, and car travel distances as well as travel times to the clinic were determined. Spatial analyses were performed to show the differences between comparison groups. In a more in-depth analysis, the proximity of SU residences to each other was examined as an indicator of possible clustering. Results During the observational period, the location of 687 inpatient and 140 IEHT unique SU were mapped using the GIS. SU receiving treatment resided predominantly within the catchment area, and this proportion was slightly higher for SU receiving IEHT than for those treated in inpatient setting (95.3% vs. 84.7%). In the catchment area, the geographical distribution of SU place of residence was similar in the two groups. There was a general higher service provision in the more densely populated communities close to Berlin. SU with residence in peripheral communities were mainly treated within the inpatient setting. The mean travel times and distances to the place of residence only differed minimally between the two groups of SU (p ; 0.1). Conclusion In especially peripheral parts of the examined catchment area, it may be more difficult to have access to IEHT rather than to inpatient services. The results raise questions regarding health equity and the planning of health care services and have important implications for the further development of intensive home treatment. Telehealth interventions such as blended-care approaches and an increase of flexibility in treatment intensity, e.g. eliminating the daily visit requirement, could ease the implementation of intensive home treatment especially in rural areas

Supplemental material for Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig

<p>Supplemental material for Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig by Sebastian Scheuer, Dagmar Haase, Nadja Kabisch, Manuel Wolff, Dagmar Haase, Annegret Haase, Nadja Kabisch, Manuel Wolff, Nina Schwarz and Katrin Großmann in Environment and Planning B: Urban Analytics and City Science</p

Enabling IoT ecosystems through platform interoperability

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.Peer Reviewe

Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes : a 5-year follow-up study

Background: Cluster analyses have proposed different diabetes phenotypes using age, BMI, glycaemia, homoeostasis model estimates, and islet autoantibodies. We tested whether comprehensive phenotyping validates and further characterises these clusters at diagnosis and whether relevant diabetes-related complications differ among these clusters, during 5-years of follow-up. Methods: Patients with newly diagnosed type 1 or type 2 diabetes in the German Diabetes Study underwent comprehensive phenotyping and assessment of laboratory variables. Insulin sensitivity was assessed using hyperinsulinaemic-euglycaemic clamps, hepatocellular lipid content using magnetic resonance spectroscopy, hepatic fibrosis using non-invasive scores, and peripheral and autonomic neuropathy using functional and clinical criteria. Patients were reassessed after 5 years. The German Diabetes Study is registered with ClinicalTrials.gov, number NCT01055093, and is ongoing. Findings: 1105 patients were classified at baseline into five clusters, with 386 (35%) assigned to mild age-related diabetes (MARD), 323 (29%) to mild obesity-related diabetes (MOD), 247 (22%) to severe autoimmune diabetes (SAID), 121 (11%) to severe insulin-resistant diabetes (SIRD), and 28 (3%) to severe insulin-deficient diabetes (SIDD). At 5-year follow-up, 367 patients were reassessed, 128 (35%) with MARD, 106 (29%) with MOD, 88 (24%) with SAID, 35 (10%) with SIRD, and ten (3%) with SIDD. Whole-body insulin sensitivity was lowest in patients with SIRD at baseline (mean 4·3 mg/kg per min [SD 2·0]) compared with those with SAID (8·4 mg/kg per min [3·2]; p<0·0001), MARD (7·5 mg/kg per min [2·5]; p<0·0001), MOD (6·6 mg/kg per min [2·6]; p=0·0011), and SIDD (5·5 mg/kg per min [2·4]; p=0·0035). The fasting adipose-tissue insulin resistance index at baseline was highest in patients with SIRD (median 15·6 [IQR 9·3–20·9]) and MOD (11·6 [7·4–17·9]) compared with those with MARD (6·0 [3·9–10·3]; both p<0·0001) and SAID (6·0 [3·0–9·5]; both p<0·0001). In patients with newly diagnosed diabetes, hepatocellular lipid content was highest at baseline in patients assigned to the SIRD cluster (median 19% [IQR 11–22]) compared with all other clusters (7% [2–15] for MOD, p=0·00052; 5% [2–11] for MARD, p<0·0001; 2% [0–13] for SIDD, p=0·0083; and 1% [0–3] for SAID, p<0·0001), even after adjustments for baseline medication. Accordingly, hepatic fibrosis at 5-year follow-up was more prevalent in patients with SIRD (n=7 [26%]) than in patients with SAID (n=5 [7%], p=0·0011), MARD (n=12 [12%], p=0·012), MOD (n=13 [15%], p=0·050), and SIDD (n=0 [0%], p value not available). Confirmed diabetic sensorimotor polyneuropathy was more prevalent at baseline in patients with SIDD (n=9 [36%]) compared with patients with SAID (n=10 [5%], p<0·0001), MARD (n=39 [15%], p=0·00066), MOD (n=26 [11%], p<0·0001), and SIRD (n=10 [17%], p<0·0001). Interpretation: Cluster analysis can characterise cohorts with different degrees of whole-body and adipose-tissue insulin resistance. Specific diabetes clusters show different prevalence of diabetes complications at early stages of non-alcoholic fatty liver disease and diabetic neuropathy. These findings could help improve targeted prevention and treatment and enable precision medicine for diabetes and its comorbidities. Funding: German Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, Research Network SFB 1116 of the German Research Foundation, and Schmutzler Stiftung