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With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

The institutional perspective is becoming an increasingly important approach for explaining sustainability behavior in an international context. Drawing on insights from institutional theory and natural environment literature, we propose that the utilization of international firms’ technological intensity to generate green innovations is conditioned by their home-country institutional profile. This article analyzes a panel of 5,024 environmental patents belonging to 80 international firms during the period 2005 to 2009. The results show that firms from countries with environmental institutional weakness reinforce their utilization of technological capabilities to generate environmental innovations in international contexts. Our results support the previous literature regarding the influence of technological intensity on the development of innovations, and we add new evidence that considers the moderating impact of the homecountry institutional profile.

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.

Background Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. Method A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. Results The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. Conclusions The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time, and therefore, they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis and assess IoT-Lite against another solution in terms of round trip time performance for query-response times. We have linked IoT-Lite with stream annotation ontology, to allow queries over stream data annotations, and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

Recruiting-related online innovations represent growing and high-potential opportunities for employers to broaden the reach of their recruiting efforts as well as reduce costs. The diffusion of innovative approaches for online recruiting, however, may experience bias due to potential employees’ lack of trust in firms offering positions online, particularly when the firms are small, operate in a risky industry, or are relatively unknown. We use the theory of reasoned action to propose that users of a corporate website develop trust beliefs with regard to three characteristics of the firm: ability, integrity, and benevolence. We have proposed three hypotheses highlighting the importance of trust generated by the corporate website in potential employees. Our experimental results involved a sample of 218 potential employees. These subjects were offered a consulting position in a fictional firm, which could only be known through its corporate website. Although previous literature has paid marginal attention to the influence of users’ beliefs regarding corporate integrity and benevolence, our findings showed that users’ perceptions of the corporate website regarding firm integrity and benevolence increased the users’ willingness to accept a job at the firm. We propose implications of our results for practitioners and for the literature of trust in online contexts.

The aim of the study is to investigate the relevance of rs1056663 and rs2708861 HUS1 polymorphisms, and rs104548, rs2981582 and rs2910164 polymorphisms of CASP8 , FGFR2 and micro RNA 146A genes, respectively, as risk modifiers in hereditary breast or ovarian cancer (BC/OC) and risk factors in sporadic BC. We performed a case–control study in 189 healthy controls (CG) and 538 BC/OC cases, 340 with familial history of BC/OC (130 carriers of BRCA1/2 mutations and 210 non-carriers) and 198 sporadic BC/OC. The polymorphisms were assessed by real-time PCR using primers and fluorescent-labelled hybridization probes. We found statistically significant differences between familial BC/OC and CG for rs1056663 and rs2708861 HSU1 polymorphisms and rs2981582 FGFR2 polymorphism, particularly in non-carriers of BRCA1/2 mutations. In this group we found statistical differences for rs1056663 HSU1 and rs2981582 FGFR2 polymorphisms ( p -trend < 0.006). The logistic regression confirmed that rs2981582 FGFR2 polymorphism (OR = 2.09; 95 % CI 1.35, 3.20) and the interaction between rs1056663 and rs2708861 HUS1 polymorphisms increased the risk of cancer (OR = 1.87; 95 % CI 1.19, 2.92). Furthermore, we found that the presence of rs1056663 and rs2708861 HUS1 polymorphisms is associated with early age of presentation of BC ( p  = 0.015) in the group of non-carriers of BRCA1/2 mutations. In addition, no association of the polymorphisms studied in sporadic BC was observed. In conclusion, the HUS1 and FGFR2 polymorphisms act as risk BC modifiers in familial BC/OC, particularly in the group of non-carriers of BRCA1/2 mutations.