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Introduction: Numerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data. Methods: Patient-, tumor- and treatment-related data were derived from the Netherlands Cancer Registry (NCR). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), scaled Brier score, and model calibration. Net benefit across applicable risk thresholds was evaluated with decision curve analysis. Results: After assessing 922 models, 87 (9%) were included for validation. Models were excluded due to an incomplete model description (n = 262 (28%)), lack of required data (n = 521 (57%)), previously validated or developed with NCR data (n = 45 (5%)), or the associated NCR sample size was insufficient (n = 7 (1%)). The included models predicted survival (33 (38%) overall, 27 (31%) breast cancer-specific, and 3 (3%) other cause-specific), locoregional recurrence (n = 7 (8%)), disease free survival (n = 7 (8%)), metastases (n = 5 (6%)), lymph node involvement (n = 3 (3%)), pathologic complete response (n = 1 (1%)), and surgical margins (n = 1 (1%)). Seven models (8%) showed poor (AUC

Noise action planning (NAP) requires collaboration among stakeholders, given the harmful health effects of noise and the subjectivity of how individuals perceive noise. Maptables can be used to mediate in such a collaborative spatial planning process. However, open software applications specifically designed for those devices are still limited or mismatched with user needs. This study presents the co-design and development process of an Open Geo-Spatial Interactive Tool (OGITO-noise) intended for maptables users. We explore to what extent such a tool can be usable and useful in supporting collaborative NAP in practice. Our methods combine agile software development and human-centered design (HCD) in a hybrid fashion, namely remote co-design meetings and face-to-face testing, to develop an open application that our intended users found useful. Those methods were utilized during a pandemic but can also be used when dealing with geographic or resource limitations.

A high equivalent isotropic radiated power (EIRP) active phased array antenna system has been designed and experimentally verified at the 28 GHz band. The phased array employs Gallium Nitride (GaN) based radio frequency front-ends with 31 dBm output power in transmit mode and 3.5 dB noise figure in receive mode. A fully metallic gapwaveguide technology has been employed in order to achieve an efficient heat dissipation per aperture area of the array as well as low-loss array antenna elements that are easily manufactured. The phased array is realized by sub-arraying an 8×8 slot array antenna with horizontal polarization. The presented antenna system is capable of analog beamforming in the range of ±60 degrees in E-plane. The presented high-bandwidth phased array antenna system is a potential candidate for high power and compact size 5G base station antennas for wireless communications requiring high temperature stability at the millimeterwave bands.

Starting from a well defined and demarcated area in space, the Corgo River, in the region of Trás-os-Montes and Alto Douro, this work seeks through Information Technologies (IT), Digital Humanities and online tools and applications (software) to provide digital images of space and water resources, anthropic elements related to water and various natural features. Based on these available resources and a set of historical cartographic documents, we provide a realistic view of the cultural and natural water landscape and how augmented reality can help archaeology interpret this landscape and its historical transformations.

We consider inequalities within households to be an important step for increasing our understanding of digital inequalities perpetuated by smart homes. We argue that the key to understanding the use of the Internet of Things is household choreographies rather than individual practices. We specifically address different power relations between household members in regard to their use of IoT devices. Using interview data collected through five visits to 30 households in 15 months, we use abductive analysis to develop our framework that has three dimensions: materiality, accessibility, and harmony. We find that acceptance of the IoT’s materiality by all household members is key, but household rules are also crucial to how well the IoT can operate. Parents can more precisely moderate their children, and data and privacy also require more moderation. In addition, the IoT can help complex household choreographies, but household choreographies that are too complex can hinder IoT use. Finally, we note that traditional gender roles remain prevalent in smart homes.

We present a case study on the application of formal methods in the railway domain. The case study is part of the FormaSig project, which aims to support the development of EULYNX — a European standard defining generic interfaces for railway equipment — using formal methods. We translate the semi-formal SysML models created within EULYNX to formal mCRL2 models. By adopting a model-centric approach in which a formal model is used both for analyzing the quality of the EULYNX specification and for automated compliance testing, a high degree of traceability is achieved. The target of our case study is the EULYNX Point subsystem interface. We present a detailed catalog of the safety requirements, and provide counterexamples that show that some of them do not hold without specific fairness assumptions. We also use the mCRL2 model to generate both random and guided tests, which we apply to a third-party software simulator. We share metrics on the coverage and execution time of the tests, which show that guided testing outperforms random testing. The test results indicate several discrepancies between the model and the simulator. One of these discrepancies is caused by a fault in the simulator, the others are caused by false positives, i.e. an over-approximation of fail verdicts by our test setup.

Maritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental influence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. The application of machine learning (ML), as one of the main subdomains of artificial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Thus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Throughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identified: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations.

Summary: The AHFS90 was developed for the prediction of early mortality in patients ≥ 90 years undergoing hip fracture surgery. The AHFS90 has a good accuracy and in most risk categories a good calibration. In our study population, the AHFS90 yielded a maximum prediction of early mortality of 64.5%. Purpose: Identifying hip fracture patients with a high risk of early mortality after surgery could help make treatment decisions and information about the prognosis. This study aims to develop and validate a risk score for predicting early mortality in patients ≥ 90 years undergoing hip fracture surgery (AHFS90). Methods: Patients ≥ 90 years, surgically treated for a hip fracture, were included. A selection of possible predictors for mortality was made. Missing data were subjected to multiple imputations using chained equations. Logistic regression was performed to develop the AHFS90, which was internally and externally validated. Calibration was assessed using a calibration plot and comparing observed and predicted risks. Results: One hundred and two of the 922 patients (11.1%) died ≤ 30 days following hip fracture surgery. The AHFS90 includes age, gender, dementia, living in a nursing home, ASA score, and hemoglobin level as predictors for early mortality. The AHFS90 had good accuracy (area under the curve 0.72 for geographic cross validation). Predicted risks correspond with observed risks of early mortality in four risk categories. In two risk categories, the AHFS90 overestimates the risk. In one risk category, no mortality was observed; therefore, no analysis was possible. The AHFS90 had a maximal prediction of early mortality of 64.5% in this study population. Conclusion: The AHFS90 accurately predicts early mortality after hip fracture surgery in patients ≥ 90 years of age. Predicted risks correspond to observed risks in most risk categories. In our study population, the AHFS90 yielded a maximum prediction of early mortality of 64.5%.

Space-borne digital elevation models (DEM) are considered as important proxy for canopy surface height and its changes in forests. Interferometric TanDEM-X DEMs were assessed regarding their accuracy in forests of Germany and Estonia. The interferometric synthetic aperture radar (InSAR) data for the new global TanDEM-X DEM 2020 coverage were acquired between 2017 and 2020. Each data acquisition was processed using the delta-phase approach for phase unwrapping and comprise an absolute height calibration. The results of the individual InSAR heights confirmed a substantial bias in forests. This was indicated by a mean error (ME) between – 5.74 and – 6.14 m associated with a root-mean-squared-error (RMSE) between 6.99 m and 7.40 m using airborne light detection and ranging (LiDAR) data as a reference. The bias was attributed to signal penetration, which was attempted to be compensated. The ME and RMSE improved substantially after the compensation to the range of – 0.54 to 0.84 m and 3.55 m to 4.52 m. Higher errors of the penetration depth compensated DEMs compared to the original DEMs were found in non-forested areas. This suggests to use the penetration compensation only in forests. The potential of the DEMs for estimating height changes was further assessed in a case study in Estonia. The canopy height change analysis in Estonia indicated an overall accuracy in terms of RMSE of 4.17 m and ME of – 0.93 m on pixel level comparing TanDEM-X and LiDAR height changes. The accuracy improved substantially at forest stand level to an RMSE of 2.84 m and an ME of – 1.48 m. Selective penetration compensation further improved the height change estimates to an RMSE of 2.14 m and an ME of – 0.83 m. Height loss induced by clearcutting was estimated with an ME of – 0.85 m and an RMSE of 3.3 m. Substantial regrowth resulted in an ME of – 0.46 m and an RMSE of 1.9 m. These results are relevant for exploiting multiple global acquisitions of TanDEM-X, in particular for estimating canopy height and its changes in European forests.

Voice Activity Detection (VAD) is a technique used to identify the presence of human voice in an audio signal. It is implemented as an always-on component in most speech processing applications. As speech is absent most of the time, this component typically dominates the overall average power consumption of the system (excluding microphone). The widespread usage in speech applications and the need for ultra low power VAD have led to a plethora of algorithms and implementations in the hardware domain, necessitating a comprehensive study and analysis to understand (real-time) requirements, different design parameters, testing strategies, but also to identify design trends, challenges and guidelines for future implementations and testing of VAD devices. A scoping review was conducted to identify the articles for hardware implementations of VAD from January 2010 -December 2021, the results of which are presented in this article. The results highlight a big design space being used for VAD along with a lack of standard testing methodology and usage of application-dependent performance metrics. An increased usage of filter-based feature extractors along with neural-network-based classifiers is observed. Due to lack of standardisation, no other trends can be established from the results. A set of rules and guidelines are therefore provided to facilitate the future development and benchmarking of VADs.

The freezing front depth (zff) of annual freeze-thaw cycles is critical for monitoring the dynamics of the cryosphere under climate change because zff is a sensitive indicator of the heat balance over the atmosphere-cryosphere interface. Meanwhile, although it is very promising for acquiring global soil moisture distribution, the L-band microwave remote sensing products over seasonally frozen grounds and permafrost is much less than in wet soil. This study develops an algorithm, i.e., the brightness temperature inferred freezing front (BT-FF) model, for retrieving the interannual zff with the diurnal amplitude variation of L-band brightness temperature (ΔTB) during the freezing period. The new algorithm assumes first, the daily-scale solar radiation heating/cooling effect causes the daily surface thawing depth (ztf) variation, which leads further to ΔTB; second, ΔTB can be captured by an L-band radiometer; third, ztf and zff are negatively linear correlated and their relation can be quantified using the Stefan equation. In this study, the modeled soil temperature profiles from the land surface model (STEMMUS-FT, i.e., simultaneous transfer of energy, mass, and momentum in unsaturated soil with freeze and thaw) and TB observations from a tower-based L-band radiometer (ELBARA-III) at Maqu are used to validate the BT-FF model. It shows that, first, ΔTB can be precisely estimated from ztf during the daytime; second, the decreasing of ztf is linearly related to the increase of zff with the Stefan equation; third, the accuracy of retrieved zff is about 5-25 cm; fourth, the proposed model is applicable during the freezing period. The study is expected to extend the application of L-band TB data in cryosphere/meteorology and construct global freezing depth dataset in the future.

Decision support systems are becoming increasingly sophisticated (e.g., being machine learning-based), attempting to automate decisions as much as possible. However, it remains challenging to extract meaningful value from large quantities of data while also maintaining transparency in seeking justification for the choices made. Instead of creating methods for increasing the interpretability of black box models, one way forward is to design models that are inherently interpretable in the first place. Rule-based methods can automate decisions with great transparency and accuracy, helping to ensure compliance with regulations and adherence to organizational guidelines. In this paper, we propose an approach that uses a decision tree machine learning classification technique for extracting business rules from IoT-generated data to predict the asset status of Smart Returnable Transport Items (SRTIs). We report on an industrial case study that uses two years of historical data, obtained from an SRTI provider in the Netherlands, to predict the status of smart pallets. We compare the performance with the results obtained by using a support-vector machine (SVM) technique. Our experiments show that our solution is both accurate and flexible in terms of business rule elicitation. The obtained decision trees are human-interpretable, can easily be combined with other decision-making techniques, and provide a prediction accuracy marginally higher than an SVM technique.

This article presents a frequency reference system that combines high frequency accuracy and low power consumption using a single-point temperature trim and batch calibration. The system is intended as a low-cost fully integrated crystal oscillator replacement. In this system, the oscillation frequency of a power-efficient, but process, voltage, temperature (PVT) and lifetime (L)-sensitive current-controlled ring oscillator (CCO) is periodically (re)calibrated by the well-behaved frequency stability of an untuned LC –based Colpitts oscillator (LCO), which is optimized for stability over PVT variations and lifetime (PVTL). During the single-point room temperature factory trim, the frequency of the LCO is determined and the result is digitally stored. An on-chip calibration engine tunes the CCO to the target frequency based on the LCO frequency, temperature sensor information, and digitally stored trimming information, thus effectively improving the frequency stability of the ring oscillator. The relatively high-power LCO is heavily duty-cycled to minimize the overall power consumption. A prototype fabricated in a 0.13- μ m CMOS SOI process and assembled in a plastic package demonstrates an inaccuracy lower than ± 93 ppm over a temperature range from − 63 ∘ C to 165 ∘ C across samples. The presented frequency reference system, including on-chip voltage regulators and a temperature sensor, occupies a chip area of 0.69 mm 2 and consumes about 64 μ A from a single 3.3-V supply. The frequency error due to supply variation is roughly 92 ppm/V. The mean frequency shift due to aging, measured before and after a six-day storage bake at 175 ∘ C, is only 52 ppm.

The interlinked nature of today’s societal challenges asks for integrative approaches. The energy transition is an especially impactful challenge and presents a compelling opportunity to pursue integration, as it requires changes to space, landscape, infrastructure and organizations at different scales. While the added value of integrative approaches that address the energy transition alongside other societal challenges is widely acknowledged, it is not the status quo. The aim of this study is to uncover the institutional barriers to integration and suggest possibilities for redesign. The paper sheds light on a hitherto relatively understudied phase of integration, namely implementation. Two illustrative cases for energy transition integration are discussed; (i) sustainable residential heating combined with climate adaptation in the urban context, and (ii) biogas production from livestock manure for rural residential heating and nitrogen reduction in the Netherlands. Inspired by the Institutional Analysis and Development framework (IAD) and networks of action situations (NAS) concept, the study shows that in the context of energy transition integration, action situations are pillarized with incidental interactions happening between sectors and across scales. The rules that govern actor interactions stem from sectoral institutional arrangements and produce sectoral action situations. Factors that especially obstruct integration are financial streams, budgeting and designated task responsibilities of actors that favour sectoral, one-dimensional projects. Actors interact in sectoral action situations and struggle to establish links to plan for more integrative outcomes. As a way forward, the study illustrates how rules can be redesigned to create integrative action situations and what mechanisms may help to achieve this in practice.

Soil moisture and soil temperature (SMST) are important state variables for quantifying the exchange of heat and water between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements are scarce on the Tibetan Plateau (TP), with even fewer are available for multiple soil depths. Tibet-Obs is such a long-term, regional-scale SMST observatory in the TP that has been established 10 years ago and includes three SMST monitoring networks, i.e. Maqu, Naqu, and Ngari (including Ali and Shiquanhe), located in the cold humid area covered by grassland, the cold semiarid area dominated by tundra, and the cold arid area dominated by desert, respectively. This paper presents a long-term (∼ 10 years) SMST profile dataset collected from the Tibet-Obs, which includes the original in situ measurements at a 15 min interval collected between 2008 and 2019 from all the three networks and the spatially upscaled data (SMups and STups) for the Maqu and Shiquanhe networks. The quality of the upscaled data is proved to be good, with errors that are generally better than the measured accuracy of adopted SMST sensors. Long-term analysis of the upscaled SMST profile data shows that the amplitudes of SMST variations decrease with increasing soil depth, and the deeper soil layers present a later onset of freezing and an earlier start of thawing and, thus, a shorter freeze–thaw duration in both the Maqu and Shiquanhe networks. In addition, there are notable differences between the relationships of SMups and STups under freezing conditions for the Maqu and Shiquanhe networks. No significant trend can be found for the SMups profile in the warm season (from May to October) for both networks that is consistent with the tendency of precipitation. A similar finding is also found for the STups profile and air temperature in the Shiquanhe network during the warm season. For the cold season (from November to April), a drying trend is noted for the SMups above 20 cm in the Maqu network, while no significant trend is found for those in the Shiquanhe network. Comparisons between the long-term upscaled data and five reanalysis datasets, including the ECMWF reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), Global Land Data Assimilation System version 2 Catchment Land Surface Model (GLDAS-2.1 CLSM), GLDAS-2.1 Noah, and GLDAS-2.1 variable infiltration capacity (GLDAS-2.1 VIC), indicate that none of the current model-based products can reproduce the seasonal variations and interannual trend changes in the measured SMST profile dynamics in both networks. All the products underestimate the STups at every depth, leading to an earlier onset of freezing and a later onset of thawing, which essentially demonstrates that the current models are not able to adequately simulate winter conditions on the TP. In short, the presented dataset would be valuable for evaluation and improvement in long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/20141567.v1 (Zhang et al., 2022).

In the face of numerous complex challenges at the ecological, economic, and social levels, Social Entrepreneurship Organizations (SEOs) offer an approach that is both solution-oriented and future-oriented by combining profitability and purpose. However, the achievement of social goals is closely linked to the ability to operate successfully in competitive environments, in which differentiation strategies, in particular the creation of strong and authentic brands, are vital to survival. Although the new paradigm of brand management, the so-called co-creative paradigm, has been extensively researched in recent decades both in the for-profit and non-profit contexts, there is still scarce empirical research addressing the field of SEOs. To exploit the potential that the co-creation paradigm offers for SEOs, our paper introduces a social impact brand model (SIBM), which sheds new light on the design process of social entrepreneurial brand meaning. The findings identify key drivers in creating SEO brands by focusing on a dual-brand core that consists of an impact mission orientation and an entrepreneurial orientation, internal branding activities, the founder's personal brand, and relevant brand (co-)creators. By aligning their brand management activities with the SIBM, SEOs can create brands that have authentic and stable brand meanings while managing stakeholder groups' various expectations.

This letter describes the design and operation of a novel type of hybrid chamber used for radiated susceptibility tests. This is a semi-anechoic chamber with the possibility to install a vibrating intrinsic reverberation chamber inside. This testing environment can be used for the execution of the radiated susceptibility tests using both the deterministic and stochastic methodologies but without changing the test location, nor moving the device under test with its auxiliary equipment. This allows test engineers to combine the advantages of both reverberation and semi-anechoic chambers with a minimum effort, making it a very practical, cost- and time-efficient solution. The electric field into the reverberation chamber is monitored by eight electric field probes, permitting the closed-loop control of the electric field strength level and an efficient real-time analysis of the performance indicators discussed in this letter.

Static energy meters installed in households, used for billing purposes of the energy consumption, have shown errors due to conducted electromagnetic interference coming from appliances in household situations. This resulted in over- and underestimations of the energy bill, and even in a perceived energy generation. The currents causing interference are known to be nonlinear pulsed currents with fast slopes. This article shows that clipping of the amplifier, following the Rogowski coil, which is used as the current sensing element, results in distorted energy measurements. Due to the pulsed nature of the current, the output of the Rogowski coil exceeds the maximum input voltage of the amplifier, resulting in clipping at the output of the amplifier. This clipped signal is then integrated in the digital chain, resulting in an offset in the perceived current. Hence, causing a higher or lower energy calculation, depending on the phase firing angle and the fastest edge of the pulse. It is found that parameters, such as the slew rate, rise and fall times, and firing angle, are correlated to the interference on static energy meters.

Increased implementation of power electronics devices as well as high penetration of micro grids and embedded generation with low-supply inertia is resulting in grid stability problems, especially in transient or startup situations. The electromagnetic compatibility of these systems with regards to transients can be difficult to diagnose due to the short time duration and often intermittent nature of events. This letter proposes a multichannel measurement system which is able to measure voltage and current waveforms in multiple locations with synchronization and sample rates in the megahertz range. The overall design, characteristics, as well as applications are showcased in this letter.

Due to the large use of resources and waste generation, the transition to a circular economy (CE) has become a major sustainability-related topic in construction. Intentions to achieve circularity are shared widely, but developments are slow in practice. This study identifies systemic barriers to the circularity transition from a social-technical systemic perspective. We used the Mission-oriented Innovation System (MIS) framework to provide insights into the problems and potential solutions underlying the circularity mission, the structure of the system and the system dynamics. Based on the analysis of a wide range of policy documents and twenty in-depth interviews with stakeholders in the Dutch infrastructure sector, three vicious cycles were identified that form persistent barriers to the transition: (1) the CE contestation cycle given the contested nature of the circularity mission; (2) the knowledge diffusion cycle given the need to adopt and diffuse knowledge; and (3) the innovation cycle when it comes to procuring and upscaling circular innovations. These barriers all relate to processual, organizational and institutional challenges rather than to technological ones. This indicates that construction managers, policymakers and researchers in the field of infrastructure circularity should shift their focus from specific circular solutions to creating appropriate conditions for changing current and introducing novel processes that facilitate circular ways of doing things.

A mixed graph is a graph that can be obtained from a simple undirected graph by replacing some of the edges by arcs in precisely one of the two possible directions. The Hermitian adjacency matrix of a mixed graph G of order n is the n×n matrix H(G)=(hij), where hij=−hji=i (with i=−1) if there exists an arc from vi to vj (but no arc from vj to vi), hij=hji=1 if there exists an edge (and no arcs) between vi and vj, and hij=hji=0 otherwise (if vi and vj are neither joined by an edge nor by an arc). Let λ1(G),λ2(G),…,λn(G) be eigenvalues of H(G). The k-th Hermitian spectral moment of G is defined as sk(H(G))=∑i=1nλik(G), where k≥0 is an integer. In this paper, we deal with the asymptotic behavior of the spectrum of the Hermitian adjacency matrix of random mixed graphs. We will present and prove a separation result between the largest and remaining eigenvalues of the Hermitian adjacency matrix, and as an application, we estimate the Hermitian spectral moments of random mixed graphs.

Liu, Y., Schuberth, F., Liu, Y., & Henseler, J. (2022). Modeling and assessing forged concepts in tourism and hospitality using confirmatory composite analysis. Journal of Business Research, 152(November), 221-230. https://doi.org/10.1016/j.jbusres.2022.07.040 --------------Funding Information: The first author is funded by the National Key R&D Program of China ( 2018YFB1403600 ). Additionally, she has been supported by a China Scholarship Council grant. The fourth author gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal) , national funding through a research grant from the Information Management Research Center – MagIC/NOVA IMS ( UIDB/04152/2020 ). He also acknowledges a financial interest in the composite-based SEM software ADANCO and its distributor, Composite Modeling. Confirmatory composite analysis (CCA) was recently proposed as a viable approach to modeling and assessing forged concepts, i.e., theoretical concepts that emerge from their components within their environment. This study introduces CCA to the field of tourism and hospitality research and shows how CCA can be conducted using estimators known from structural equation modeling (SEM) with latent variables as implemented in common SEM software. It shows how emergent variables can be employed to model forged concepts and how CCA can be used for assessing them. In doing so, we explain the four major CCA steps comprising (1) model specification, (2) model identification, (3) model estimation, and (4) model assessment. To illustrate and guide scholars in applying CCA, we provide an empirical example from the field of tourism and hospitality research. publishersversion publishersversion published

Complex project networks confront project actors, with value pluralism originating from a plethora of competing organizational and institutional systems related to the project environment. Using a single urban redevelopment case study, we present a dynamic understanding of the emergence and nature of conflicts in different conflict arenas of collaborative project networks. We identify seven coping patterns to reduce and engage with this complexity in the process of delivering value throughout projects. The coping patterns enable a more dynamic and flexible approach toward conflict management in project networks with a high degree of conflicting project interests.

Favelas are the most common type of informal settlements found in Brazil. The Housing Secretariat, City Hall, Sao Paulo, has conducted surveys using Unmanned Aerial Vehicles (UAVs) for the favelas to facilitate the slum upgrading projects and has taken the initiative to create a digital twin of the slum areas. This study illustrates the feasibility of developing a methodological workflow to create a digital twin by automatic 3D building reconstruction in slums from the UAV point cloud and the 2D building footprints with a continuous link for updating the building semantic information. This study focuses on facilitating data integration and updating the semantic information into the 3D model to provide additional information about the individual buildings in the slums. The assessments concluded that the proposed workflow is suitable for creating digital twins for slums based on the UAV and 2D cadastral data. However, the 3D slum model had a few limitations, which are discussed in this paper.

This article presents the degradation effects of long-term use of a dual vibrating intrinsic reverberation chamber (dual VIRC) for shielding effectiveness (SE) measurements. At the beginning with a new fresh dual VIRC setup it is possible to achieve a very high dynamic range (DR) but the DR degrades due to long-term use of the dual VIRC. The effect of 10 years use of the dual VIRC on the DR is shown as function of usage time. Also, the effect on the DR of a new dual VIRC with better conductive cloth has been presented.

Humans are capable of managing multiple tasks simultaneously. It is widely assumed that human motor control can be emulated by impedance control. To achieve human-like behavior, however, the impedance parameters of multiple tasks may vary during task execution. We propose an algorithm that shapes task impedance as a function of the robot's time-varying inertial properties. These properties involve virtually constrained masses and virtually constrained inertias that counteract a task in order to comply with a given constraint. In this work, we not only detect task conflicts, but also show how to handle them. Our method is able to control kinematically redundant robots. We developed a damping-design method that does not interfere with our desired Cartesian task-space behavior. The control approach was verified in experiments on a real robot. We compared our impedance shaping method with two alternative control approaches: simple impedance superposition and nullspace projection. Our method preserved the passivity while improving the Cartesian task performance of an impedance controller. The method has computational advantages, beneficial to control robots with many degrees of freedom.

Unemployment and precarious employment have been treated as liminal states where people’s everyday lives are rendered into an “in-between” position, a place where the uncertainty of meaning and decision-making prevail. Liminality has also been applied as a way of understanding a changing social world, implying a transformation in consciousness emanating from prolonged indeterminism. To develop a clearer understanding of coping with liminal status, we used a narrative futuring approach (i.e., a reflexive practice to elicit narratives from the viewpoint of a desired future). Twenty “letters from the future” were written by unemployed and precariously employed young adults with higher education degrees living in Crete. The letters engaged the participants in the process of critical reflection, where they imagined eutopian lifeworlds by attempting to place the prevalent “crisis story” in the background, embracing or confronting liminal status. Implications focus on how the narrative future-making approach can be applied to disentangle the complexities involved in understanding liminal states and their inherent transformative potential where both personal, interpersonal, and the broader social and societal context come into awareness.

Dutch energy suppliers are obliged to cooperate in reducing consumption by stimulating energy saving behaviours among their customers. One important strategy for this context is personalized gamification. We investigate how a personalized gamified energy saving application can be designed based on individual preferences within the context of an energy supplier. To design a personalized gamification application the proposed method of Knutas et al. was adapted by leaving out the parts about machine learning and adding an evaluation phase. We show that customization by users can be a valuable alternative for using machine learning personalization strategies. We used this iterative design process to focus on personalizing, in this use case for the three main user types of the customer base of a Dutch energy supplier. The design of the gamified application was adapted using feedback of both stakeholders and customers using interviews and focus groups. This resulted in two final designs designed to stimulate energy saving behaviour among customers. The design had a dashboard which allowed for personalization within gamified elements, and an energy editor in which users can change characteristics in their households to learn about the effects of actions on their energy consumption. These two final designs were validated using an interactive prototype along with interviews. The added value of this study is that it shows that the Hexad framework with its proposed gamification user types is suitable to understand the main motivations of a target group. Results suggest that designing within gamified mechanics based on a user type’s main motivation is an effective strategy for personalization. Motivation-based design is not the only successful personalization design strategy but adapting designs to personal situation and existing energy saving behaviour might also motivate users.

This study investigated the effects of providing domain information in an early stage of an inquiry process, together with an aligned hypothesis scratchpad, on inquiry learning, and hypothesis generation in particular. Participants were provided with basic domain information that was adapted to their prior knowledge (experimental condition) or received no introduction to the domain (control condition) before writing their hypotheses. Sixty-nine secondary school students from two countries were randomly assigned to the experimental or the control condition. These two conditions were compared on hypothesis generation, the subsequent inquiry processes of data recording and drawing conclusions, and knowledge acquisition. Results indicate that the supported students could specify more testable relations in their hypotheses, and could write hypotheses with higher levels of informativeness about variables, conditions, and relations. No differences between conditions were found on data recording, drawing conclusions, and knowledge acquisition. Limitations and directions for future research are presented.

Artificial agents that support people in their daily activities (e.g., virtual coaches and personal assistants) are increasingly prevalent. Since many daily activities are social in nature, support agents should understand a user's social situation to offer comprehensive support. However, there are no systematic approaches for developing support agents that are social situation aware. We identify key requirements for a support agent to be social situation aware and propose steps to realize those requirements. These steps are presented through a conceptual architecture centered on two key ideas: (1) conceptualizing social situation awareness as an instantiation of `general' situation awareness, and (2) using situation taxonomies for such instantiation. This enables support agents to represent a user's social situation, comprehend its meaning, and assess its impact on the user's behavior. We discuss empirical results supporting the effectiveness of the proposed approach and illustrate how the architecture can be used in support agents through two use cases., Comment: 8 pages, 1 figure

The Bot Usability Scale (BUS) is a standardised tool to assess and compare the satisfaction of users after interacting with chatbots to support the development of usable conversational systems. The English version of the 15-item BUS scale (BUS-15) was the result of an exploratory factorial analysis; a confirmatory factorial analysis tests the replicability of the initial model and further explores the properties of the scale aiming to optimise this tool seeking for the stability of the original model, the potential reduction of items, and testing multiple language versions of the scale. BUS-15 and the usability metrics for user experience (UMUX-LITE), used here for convergent validity purposes, were translated from English to Spanish, German, and Dutch. A total of 1292 questionnaires were completed in multiple languages; these were collected from 209 participants interacting with an overall pool of 26 chatbots. BUS-15 was acceptably reliable; however, a shorter and more reliable solution with 11 items (BUS-11) emerged from the data. The satisfaction ratings obtained with the translated version of BUS-11 were not significantly different from the original version in English, suggesting that the BUS-11 could be used in multiple languages. The results also suggested that the age of participants seems to affect the evaluation when using the scale, with older participants significantly rating the chatbots as less satisfactory, when compared to younger participants. In line with the expectations, based on reliability, BUS-11 positively correlates with UMUX-LITE scale. The new version of the scale (BUS-11) aims to facilitate the evaluation with chatbots, and its diffusion could help practitioners to compare the performances and benchmark chatbots during the product assessment stage. This tool could be a way to harmonise and enable comparability in the field of human and conversational agent interaction.

With the increase of frequency and intensity of heavy precipitation in the future, rainfall triggered landslides (RTL) can be one of the major threat to human life and property security. Early warning systems of natural hazards are one of the most effective measure for reducing disaster losses and risks. However, the forecast of RTL in near-real-time (NRT) is extremely difficult since the quality of NRT precipitation data is relatively poor. Quantile regression forest (QRF), a state-of-the-art statistical postprocessing method, has been proved to reduce the difference existing between NRT satellite precipitation estimates and ground-based rainfall data. When predicted rainfall maps are put side by side with raw NRT satellite product, the pattern of the first matches much more closely the locations where landslide events have been mapped in a test site in North-Eastern Turkey. This leave an optimistic perspective on the application of statistical postprocessing techniques in the field of weather science and in general for natural hazard assessment. Ideally, by correcting the continuous information in space and time provided by satellite rainfall estimates, one could create a new operational tool for landslide early warning system, not bound to the financial and deployment requirement typical of rain gauge and terrestrial radar stations.

The revegetation practice is one of the most efficient ways to alleviate soil erosion and desertification. However, the land cover change can considerably disturb ecohydrological processes, particularly in arid and semiarid regions where ecosystems are fragile and suffer intense water stress. This study evaluated the effects of revegetation on the energy, water and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, Northwest China, by simulating two scenarios of shrubs-grassland and grassland ecosystem with the STEMMUS-SCOPE model. The STEMMUS-SCOPE model integrates canopy photosynthesis, fluorescence, energy balance model and soil water and heat transfer model in the soil-plant-atmosphere continuum system. The model was validated by field observations from May to September of 2016-2019, and showed good performances in simulating the energy, water and carbon fluxes. It indicated that the revegetation facilitated carbon fixation (+69.34%). Latent heat flux was the primary consumer of the available energy and was stronger in the shrubs-grassland ecosystem (+16.76%). With the remarkably increased transpiration of the shrubs-grassland ecosystem (+86.72%), revegetation intensified the soil water losses, especially the soil water content within the 0-200 cm depth (−18.97%). Moreover, the water consumption of the shrubs-grassland ecosystem tended to exceed the received precipitation over the growing seasons. These results emphasized the necessity of considering the adverse impacts of revegetation in future ecological restoration, especially the irreversible soil water depletion and imbalance of energy, water and carbon cycles.

Climate projections strongly suggest that the 2022 sweltering summer may be a harbinger of the future European climate. Climate extremes (e.g., droughts and heatwaves) jeopardize terrestrial ecosystem carbon sequestration. The construction of an open digital twin of the soil-plant system helps to monitor and predict the impact of extreme events on ecosystem functioning and could be used to recommend measures and policies to increase the resilience of ecosystems to climate-related challenges. A digital twin refers to a highly interconnected workflow, with a data assimilation framework at its core to combine observations and process-based models, meanwhile accompanied by an interactive and configurable platform that allows users to create and evaluate user-specific scenarios for scientific investigation and decision support. Creating an open digital twin means creating a digital twin following Open Science and FAIR principles, both for data and research software. In this contribution, the STEMMUS-SCOPE model was used as an example to develop an open digital twin of the soil-plant system. We suggest our recently developed open digital twin infrastructure could serve as the backbone for an interoperable framework to facilitate the digitalization of other Earth subsystems (e.g., by simply replacing the soil-plant model). In addition, we show how software not designed initially as open can be adopted to create an open digital twin using containers - standardized computational environments that can be shared, reused and that foster reproducibility.

Accurate estimation of carbon assimilation and allocation plays a significant role in the plant growth and terrestrial ecosystems. The STEMMUS-SCOPE model integrates photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, and has good performances in estimating water, energy, and carbon fluxes. However, the plant growth states (i.e., leaf area index (LAI) and plant height (PH)) are needed as inputs for running the STEMMUS-SCOPE model, and are obtained either from interpolating observations or taking as constants over the time. As a result, the physical interactions are not adequately captured between radiative transfer, plant growth and soil water movements. The objective of this study is to consider the plant growth in STEMMUS-SCOPE model via coupling a crop growth module (i.e., WOFOST module). The coupled STEMMUS-SCOPE-WOFOST model was evaluated with plant functioning measurements. The results indicate that the simulation of LAI and PH is significantly improved and consistent with the dynamic of the water stress and gross primary production (GPP). Besides, the additional generated state variables (i.e., the biomass of root, leaf, stem as well as yield) can also agree well with the observations. Finally, the interactions between the land surface fluxes, soil moisture dynamic and plant growth are all well simulated. The STEMMUS-SCOPE-WOFOST model provides a mechanistic window to link the satellite observation of solar-induced fluorescence to above- and below-ground biomass, land surface fluxes, and root zone soil moisture, in a physically consistent manner.

A solid understanding of the global water cycle and how land surface processes respond to both changes in climate and pressure due to water use is essential for society. Although Land Surface Models (LSM) and Global Hydrological Models (GHM) are able to simulate the spatiotemporal variability of the water balance relatively reliably, intercomparison studies have indicated considerable differences between the models. Each LSM and GHM present a unique set of equations, parameters and configurations that contribute to the spread of simulated hydrological responses to meteorological forcings. In order to improve our understanding of modeling uncertainties, we propose a variable importance assessment for 5 LSM/GHM (JULES, HTESSEL, PCR-GLOBWB, SURFEX and ORCHIDEE) from the EartH2Observe (E2O) project. The output of the models and the meteorological forcings were collected from the Water Resources Reanalysis Tier 2 of the E2O project, which consists of a global dataset with spatial resolution of 0.25ox0.25o. We used soil texture and land cover datasets that most resemble the inputs used by each LSM/GHM during the E2O project. The models’ outputs were used to estimate 6 hydrological indices for every land cell: Evaporation-Precipitation ratio; Runoff-Precipitation ratio; Surface Runoff-Total Runoff ratio; median Soil Moisture variation caused by a Rainfall event; median Surface Runoff caused by a Rainfall event; and Soil Moisture temporal autocorrelation. Then, we evaluate the input features (meteorological, land cover, and soil texture) importance to the hydrological indices of each model using machine learning. With the analysis we aim to examine a) How much the models differ and why? b) To what extent are the output differences related to the input features or/and to the models formulation? and c) How significant is each feature to the respective hydrological index?

Soil water availability is a critical requirement for vegetation functioning in a water-limited regime. Vegetation takes up water from varying soil depths depending on their rooting location and soil moisture availability. The uptake depth varies spatially across climate regimes and vegetation types and temporally between seasons. Yet, a scientific consensus on the global relevance of near-surface and sub-surface soil moisture for vegetation functioning is still lacking and is the focus of this study. In particular, we calculate the correlation between the Near-Infrared Reflectance of Vegetation (NIRv) with both satellite-derived near-surface soil moisture from ESA-CCI and terrestrial water storage from GRACE. This is done globally and with monthly data during the growing season at each grid cell and accounting for the confounding effects of temperature and radiation. We analyze how these correlations vary spatially across varying vegetation types and climatic regimes, and temporally between all growing season months and particularly dry months. Finally, we repeat the analyses using Sun-induced fluorescence (SIF) data instead of NIRv. We find that NIRv and SIF correlate more strongly with near-surface soil moisture compared to terrestrial water storage in semi-arid regions with low tree cover. This suggests that the vegetation preferentially takes up water from near-surface soil moisture whenever available to meet its transpiration demand. In contrast, in regions with more tree cover and in drier regions, the correlation with terrestrial water storage is comparable to or even higher than with near-surface soil moisture. This indicates that trees can make use of their deep rooting systems to access deeper soil moisture resources, similar to vegetation in arid regions. In particularly dry months, correlations with near-surface soil moisture increase while this is even more the case with terrestrial water storage, highlighting the relevance of deeper water resources during rain-scarce periods.Overall, while direct observations of sub-surface soil moisture are scarce, this study employs different satellite-based data streams in order to estimate the relevance of near-surface versus sub-surface soil moisture for vegetation functioning. This can inform the representation of vegetation-water interactions in land surface models to support more accurate climate change projections.

The current practice of data dissemination via data portals exhibits scalability limitations. Technically, the use of keywords and themes for the description of data semantics cannot keep up with the growing number and topical diversity of published datasets. At the same time, the publish-find-bind model of data dissemination decouples data discovery from data access which makes it increasingly difficult to find data that can be (re)used in combination with other data for the construction of tailored data products. However, data application developers increasingly need to see all the data not only from one provider but also from several providers as a whole in runtime. In this context, Linked Data (LD) as a technique for publishing structured semantically rich data on the Web is able to open up the data silos of the traditional data portals. Being based on well-known Web technologies (such as HTTP), interoperable semantic standards and graph data model, LD makes it possible to perform data discovery, access and integration in one step. Apart from that, LD creates a Web-scale semantic infrastructure that enables interoperability not only between separate databases but also between organisations. However, converting existing datasets into LD as is does not allow the realisation of these properties. In fact, original data sources were created with the closed world assumption in mind and, therefore, lacked connections between each other and the existing semantic infrastructure. For this reason, the realisation ofLDadvantages requires data to be semantically enriched, which is a knowledge and labour-demanding task. Additionally, data providers meet an organizational challenge. Investment in semantic enrichment (SE) of provider data to leverage LD requires evaluation of benefits that are difficult to quantify. A common and well-understood approach of use-case-driven development is too narrow to cover all business requirements and, therefore, is not adequate to demonstrate the added value of LD. Instead, this thesis proposes enabling exploratory search (ES) of data as an integrated approach towards developing LD in an organisation. This proposition is supported by two major research contributions presented in this work, namely (1) a business rationale for LD implementation and (2) technical requirements of SE instantiated by a number of developed data and software artefacts. Technical requirements of SE are framed as four data enrichment levels: (1) ontological, (2) conceptual, (3) instantial and (4) literal. Semantic enrichment design principles for each of these levels are created based on the analysis of the data-related requirements and functional limitations of existing ES systems. The analysed systems are grouped into nine categories according to the exploratory techniques they facilitate. This makes it possible to abstract from particular implementations and formulate requirements for each of the categories. The business rationale for LD implementation is based on the analysis of the roles that LD plays in the realisation of a business vision of a large public data provider. Deriving from the experience of the development of a data platform at the Netherlands’ Cadastre Land Registry and Mapping Agency (Kadaster), it is concluded that LD directly contributes to the operationalisation of three out of four business ambitions, namely, (1) enabling a use-case oriented vision, (2) increasing the data value, and (3) ensuring the certainty of the data and legitimacy of the organization. The remaining ambition of being a spatial data provider can be fulfilled by conventional technologies, however, LD indirectly contributes to reaching users outside of the GIS community. These findings are used to prioritise the previously identified technical requirements of SE to achieve alignment with the business vision. As a result of this prioritisation, two semantic enrichment scenarios that combine design principles into two groups according to their advancement level are formulated, implemented, and evaluated. The driving reason for the division of scenarios is to differentiate design principles that are easy to implement but that lead to high gains in terms of ES (the first scenario) from those that require greater efforts but at the same time lead to maximum possible results (the second scenario). The first scenario focuses on ES-enabling semantic enrichment of datasets that have thematically similar content but are curated by several independent data providers. The design principles covered in the first scenario allow to create and disseminate LD representation of existing data on top of existing spatial data infrastructures (SDIs) without altering already implemented data pipelines and structures. They are put to test during the development of the Open European Location Services (OpenELS) project, a collaborative effort aimed at the construction of European-level SDI for the provision of geospatial data within the Spatial Information in the Europe Community (INSPIRE) initiative. The second scenario, in contrast, covers the creation of explorable Knowledge Graphs, voluminous data sources that combine datasets with a wide range of topics from a multitude of knowledge domains. Apart from the topical diversity, the design principles of this scenario also cater for a large volume of spatio-temporal data by utilising topological relations for the creation of hierarchical spatial partitioning. Construction of the Kadaster Knowledge Graph (KKG) by combining 12 datasets curated by 8 organisations represented the evaluation of the second scenario design principles. The usability of the graph was assessed during the creation of three applications, namely data browsing, urban planning, and the development of a chatbot. There are several important characteristics of the research that influence the applicability of research contributions. The first limitation stems from the fact that a major part of the research is conducted in a governmental institution, namely Kadaster. Therefore, for for-profit organisations, the business rationale for LD implementation has relevance in parts related to revenue-focused business ambitions such as use case orientedness and data value increase. Another limitation is that the rationale is based on qualitative reasoning which implies that it does not provide an understanding of how much resources implementation of LD can take. For the data and software artefacts created as a part of this research, it is important to emphasise that their evaluation is based on providing semantic enrichment and exploratory search capabilities and does not include usability testing. However, in this respect, the design principles of both scenarios were discussed within a large group of specialists involved in their implementation, then taken into use within their organisations, and considered applicable and useful. The approach presented in this thesis is thought to be beneficial for the following application domains and target groups. First of all, the research contributes to the topics of data dissemination and data- and semantic- infrastructure development. Therefore, it is of interest to data owners, data custodians and data providers including but not limited to National Mapping and Cartographic Agencies (NMCAs), since the research is greatly concerned with the utilisation of spatial and temporal data components for linking and consolidating data assets into one virtual data source. This can be particularly appealing for data scientists and engineers who are confronted with tailored data product development as well as application developers who work with heterogeneous cross-domain data to create novel applications. From the viewpoint of software development companies, this work can be considered as a blueprint for semantic enrichment scenarios to be supported by data management tools, and as such, it can help in foreseeing market demands in terms of functional requirements. Apart from that, the research findings add to the business rationalisation of semantic enrichment in organisations. Therefore, managers who are responsible for data desilofication strategies and business developers who are concerned with the development and operationalisation of the business vision are among the target groups of this work. Exploratory search over big and distributed data is another application domain of the thesis, therefore the analysis of ES system requirements towards data quality is of particular interest to the community of ES researchers and software developers. Finally, elaboration on the topics of Knowledge Graphs development presented in this work contributes to the ongoing discussion in the Semantic Web and Linked Data communities and therefore, is of interest to them.

When using an exoskeleton, it is important to measure the interaction force and user intention. To accomplish this, force and surface electromyography (sEMG) sensors often are integrated into the exoskeleton. However, this requires complex parts and exoskeletons are often made in low quantities. Therefore, 3D printing these exoskeleton parts with integrated sensors is of potential interest. In order to do that, three things need to be researched. The first research topic is a multi-material fabrication process that is able to print sensors and the embedding parts in one go. Therefore an overview of the currently available techniques for extrusion-based printing and the remaining challenges is given. Next, some remaining challenges will be addressed. These are: the calibration of multiple printheads relative to each other and how to check the quality of layers of 3D printed conductors. The second research topic is a way to 3D print usable force sensors. There are two common ways to do this; one is to make a resistive sensor and the other is to make a capacitive sensor. Resistive sensors are easy to print but are often non-linear and have large hysteresis. A way to linearize these sensors by using differential measurements and a way to reduce the hysteresis by using a model are presented. Another way to make more linear sensors is to fabricate capacitive sensors. In this work, it is shown that with a flexible dielectric and a simple parallel plate structure sub-newton sensitivities can be achieved. Subsequently four of these sensors are combined to form a sensor that can measure both shear and normal forces. It was also noted that these sensors have a position-dependent response at high frequencies because of the high electrode resistance. This allowed the measurement of not only the force but also the position of the force by looking at the real and imaginary parts of the impedance of the sensors. The third research topic is a way to print, connect and read out sEMG electrodes. Printing the electrodes is relatively straightforward. However, they form a dry electrode-skin interface and therefore they have a higher impedance than regular AgCl electrodes, imposing higher demands on the amplifier input impedance. Also, the electrodes need to stay in contact with the skin during movement to prevent the resulting signal from containing motion artifacts. Therefore, an amplifier that contains several techniques to improve the input impedance and a method that enables simultaneously measurement of the electrode impedance was developed. To connect the sEMG electrodes to the amplifier, pogo pins that connect to silver ink traces in the print were added to the amplifier. These pogo-pins allow a solderless connection of the sensors. Yet another challenge is that only limited information can be obtained with a low number of 3D printed or conventional sEMG electrodes. Therefore, the use of multifrequency electrical impedance myography (mfEIM) is also explored. To do this, a multifrequency spectrometer that can measure both the sEMG and the mfEIM was developed. Then this spectrometer was connected to subjects performing tasks in a one degree of freedom exoskeleton that measures joint angle and joint torque. These measurements showed that mfEIM can provide additional information about the joint angle. By researching these three research topics, 3D printed parts with integrated sensors are one step closer to reality.

The popularity of the Internet of Things (IoT) devices has been gaining interest amongst consumers. The rise of consumers benefiting from IoT devices has increased the threat of cyber-attacks. The safety, security, and privacy of consumers can be negatively affected if vulnerabilities of IoT devices are exploited. Therefore, there is a need of understanding what within IoT devices is necessary to secure. Further, the implementation of necessary and important requirements is needed to ensure protection against cyber-attacks on IoT devices. The recently published Cyber Security for Consumer Internet of Things (CSCIoT) standard, called ETSI EN 303 645, is a global standard that describes requirements on implementing a minimum level of security for IoT devices. This paper evaluates the sufficiency of cyber security of the consumer IoT standards’ requirements and gradation. The evaluation is done by comparing CSCIoT to the international professional IoT standard, called IEC 62443, and with the other related work, such as the Secure by Design report of the UK Department for Digital, Culture Media & Sport. Also, this paper discusses implications regarding consumer responsibility on security. This paper aims to stimulate more precision and extension of requirements for consumer IoT devices to lower the risk of cyber-attacks.

This study examined data and strategic skill-related problems that activity tracker users experience and the extent to which these problems vary by gender, age and educational attainment. A performance test (N = 100) was conducted to study problems experienced during actual use of activity trackers. Video data of participants’ screen actions were analyzed by coding for skill-related problems regarding data retrieval and interpretation, and goal setting and decision making. The results revealed that both data and strategic skill-related problems are experienced by all users, but are particularly prominent amongst elderly and less educated users. Problems were mostly related to retrieving the correct data. Additionally, substantial problems were experienced in every facet of strategic use. Altogether, data and strategic skills are underdeveloped for the beneficial use of activity trackers. Moreover, the differences in the problems experienced among users cause widening digital inequalities.

Most deep learning-based image classification models are vulnerable to adversarial attacks that introduce imperceptible changes to the input images for the purpose of model misclassification. It has been demonstrated that these attacks, targeting a specific model, are transferable among models performing the same task. However, models performing different tasks but sharing the same input space and model architecture were never considered in the transferability scenarios presented in the literature. In this paper, this phenomenon was analysed in the context of VGG16-based and ResNet50-based biometric classifiers. The authors investigate the impact of two white-box attacks on a gender classifier and contrast a defence method as a countermeasure. Then, using adversarial images generated by the attacks, a pre-trained face recognition classifier is attacked in a black-box fashion. Two verification comparison settings are employed, in which images perturbed with the same and different magnitude of the perturbation are compared. The authors’ results indicate transferability in the fixed perturbation setting for a Fast Gradient Sign Method attack and non-transferability in a pixel-guided denoiser attack setting. The interpretation of this non-transferability can support the use of fast and train-free adversarial attacks targeting soft biometric classifiers as means to achieve soft biometric privacy protection while maintaining facial identity as utility.

Nowadays, 3D thermal models can play an important role in buildings' energy management while acquiring multisource data to generate a high-resolution 3D thermal model. Consequently, in this article, a method for intensifying 3D thermal model using deep learning-based image super-resolution is presented. In the proposed method, first, the enhanced deep residual super-resolution (EDSR) deep network is re-trained based on thermal aerial images. Second, the resolution of low-resolution thermal images is enhanced using the newly trained network. Finally, the state-of-the-art structures from motion (SfM), semi global matching (SGM) and space intersection are utilized to generate intensified 3D thermal model from the resolution enhanced thermal images. Spatial evaluations indicate a 5% increase in edge-based image fusion metric (EFM) for the intensified 3D model. Besides, the evaluations show that the modulation transfer function (MTF) curves of the intensified 3D thermal model are closer to a reference model against the original 3D thermal model. Highlights A 3D thermal model intensification solution using EDSR is proposed which is independent of hardware techniques and multisource data. Considering the importance of edge sharpness in the intensified 3D thermal model, the quality of edges is assessed using MTF curves and the EFM metric. In comparison to the original 3D thermal model, the MTF curves of the intensified 3D thermal model are closer to the MTF curve of the high-resolution 3D model. The EFM metric shows higher values for MTF curves of the intensified 3D thermal model against MTF curves of the original 3D thermal model.

“All real living is meeting”. This study presents an empirical study of the way a sense of meaning-as-connectedness is brought about in a group counseling intervention in which meaningful moments are shared. Results from a thematic analysis exemplify and at one point extend Buber's philosophy of meeting.

Using a new propagation measurement set-up, which produces a high number of data, enabling a proper statistical analysis, and resulting in very concise results, propagation measurements were performed and analized at 16 residential locations in The Netherlands in the frequency range from 1.8 to 28 MHz. In the whole frequency range the propagation loss appear to be higher than according the International Telecommunication Union ground-wave propagation model might be expected. Also typical characteristics of that model are not present, but instead the propagation shows a constant roll-off in dBs per decade, which slope is increasing with frequency. A regression curve could be established, and constants filled in. This statistical information may be used for building an accumulation model to lay a causality between source powers, source densities, and local Man-Made Noise levels.

Ultrasound (US) contrast agents consist of microbubbles ranging from 1 to 10 μm in size. The acoustical response of individual microbubbles can be studied with high-frame-rate optics or an “acoustical camera” (AC). The AC measures the relative microbubble oscillation while the optical camera measures the absolute oscillation. In this article, the capabilities of the AC are extended to measure the absolute oscillations. In the AC setup, microbubbles are insonified with a high- (25 MHz) and low-frequency US wave (1–2.5 MHz). Other than the amplitude modulation (AM) from the relative size change of the microbubble (employed in Renaud, Bosch, van der Steen, and de Jong (2012a). “An ‘acoustical camera’ for in vitro characterization of contrast agent microbubble vibrations,” Appl. Phys. Lett. 100(10), 101911, the high-frequency response from individual vibrating microbubbles contains a phase modulation (PM) from the microbubble wall displacement, which is the extension described here. The ratio of PM and AM is used to determine the absolute radius, R0. To test this sizing, the size distributions of two monodisperse microbubble populations ([Formula: see text] 2.1 and 3.5 μm) acquired with the AC were matched to the distribution acquired with a Coulter counter. As a result of measuring the absolute size of the microbubbles, this “extended AC” can capture the full radial dynamics of single freely floating microbubbles with a throughput of hundreds of microbubbles per hour.

Creating concept maps is considered to be a powerful means for learning. It requires students to systematically organize and integrate their knowledge, which can foster meaningful learning. However, students scarcely spontaneously engage in the (meta)cognitive processes necessary for effective knowledge integration, such as reflection, which can hamper the effectiveness of concept mapping for learning. This study explores the effect of additional support that stimulates reflection by means of expert examples, reflection prompts, and classroom discussion. First-year technical vocational students (N = 144, Mage = 17.5 years) studied electricity-related topics in an online learning environment and created concept maps about their knowledge. Students’ concept maps were supplemented with either (1) no support (control condition), (2) an expert example (a combined concept map, containing their own and an expert example concept map, with differences highlighted), (3) an expert example and reflection prompts students had to process individually, (4) an expert example and reflection prompts students had to process individually after a teacher-guided classroom discussion. Students in the classroom discussion condition showed higher learning gains compared to all other conditions. This can be explained by the quality of their reflection, which proved to be a significant predictor of learning gain.

This study proposes an exact model for timetable recovery after disturbances in the context of high-frequency public transport services. The objective of our model is the minimization of the deviation between the actual headway and the respective planned value. The resulting mathematical program for the rescheduling problem is nonlinear and non-smooth; thus, it cannot be solved to optimality. To rectify this, we reformulate the model using slack variables. The reformulated model can be solved to global optimality in real-time with quadratic programming. We apply the model to real data from the red metro line in Washington D.C. in a series of experiments. In our experiments, we investigate how many upstream trips should be rescheduled to respond to a service disturbance. Our findings demonstrate an improvement potential of service regularity of up to 30% if we reschedule the five upstream trips of a disturbed train.

Video games and game-like experiment tasks are increasingly used to study human behavior. Prior work used such video games to study foraging behavior, including stressors such as hostile entities (i.e. predators). For this study we adapted a 3D video game to assess its potential for studying the impact of hostile environments on human foraging behavior. Two versions of the game were created that varied in terms of hostility, with the hostile condition including predatory non-player characters (NPCs). This pilot aimed to assess the suitability of the modified game for inducing and evaluating stress-related foraging patterns and pinpoint needed modifications for a large-scale study. Participants (n = 18) reported higher levels of stress in the hostile compared to the non-hostile environment. Results of the pilot indicate that modifications are needed to induce more pronounced behavioral effects, as the currently used measures of exploration (e.g., distance traveled; number of gathered items) were not affected by the hostility manipulation. We aim to further develop the game to improve effectiveness of the hostility manipulation and to investigate the effects with more elaborate exploration analyses (e.g., including measures of entropy).

The new, two-way coupled, distributed and transient MARMITES-MODFLOW (MM-MF) model, coupling land surface and soil zone domains with groundwater, is presented. It implements model-based partitioning and sourcing of subsurface evapotranspiration (ETss) as part of spatio-temporal water balance (WB). The partitioning of ETss involves its separation into evaporation (E) and transpiration (T), while the sourcing of E and T involves separation of each of the two into soil zone (Esoil and Tsoil) and groundwater (Eg and Tg) components. The objective of that development was to understand the system dynamics of a catchment with shallow water table, through spatio-temporal quantification of water fluxes and evaluation of their importance in water balances, focusing on the Eg and Tg components of ETss. While the Eg is computed using formulation from published study, the Tg is obtained through a novel phenomenological function, based on soil moisture availability and transpiration demand driven by climatic conditions. The MM-MF model was applied in the small La Mata catchment (~4.8 km2, Salamanca Province, Spain), characterized by semi-arid climate, granitic bedrock, shallow water table and sparse oak woodland. The main catchment characteristics were obtained using remote sensing, non-invasive hydrogeophysics and classical field data acquisition. The MM-MF model was calibrated in transient, using daily data of five hydrological years, between 1st October 2008 and 30th September 2013. The WB confirmed dependence of groundwater exfiltration on gross recharge. These two water fluxes, together with infiltration and Esoil, constituted the largest subsurface water fluxes. The Eg was higher than the Tg, which is explained by low tree coverage (~7%). Considering seasonal variability, Eg and Tg were larger in dry seasons than in wet season, when solar radiation was the largest and soil moisture the most depleted. A relevant observation with respect to tree transpiration was that during dry seasons, the decline of Tsoil, associated with the decline of soil moisture, was compensated by increase of Tg, despite continuously declining water table. However, in dry seasons, T was far below the atmospheric evaporative demand, indicating that the groundwater uptake by the tree species of this study constituted a survival strategy and not a mechanism for continued plant growth. The presented MM-MF model allowed to analyze catchment water dynamics and water balance in detail, accounting separately for impacts of evaporation and transpiration processes on groundwater resources. With its unique capability of partitioning and sourcing of ETss, the MM-MF model is particularly suitable for mapping groundwater dependent ecosystems, but also for analyzing impacts of climate and land cover changes on groundwater resources.