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Background Patients infrequently mobilize at the surgical ward after cardiac surgery. Inactivity results in prolonged hospital stay, readmissions and increased cardiovascular mortality. Next, the course of in-hospital mobilization activities for patients is unclear. The aim was to evaluate early mobilization after heart surgery with a mobilization poster on the Activity Classification Guide for Inpatient Activities score from the American College for Sports Medicine (ACSM). Second, to develop a Thorax Centrum Twente (TCT) score to assess distinctive activities performed. Methods A poster was developed for the Moving is Improving! study to stimulate hospital mobilization after heart surgery. In this sequential-group study at a cardiothoracic surgery ward, 32 patients were included in the usual care group and 209 patients in the poster mobilization group. Change of ACSM and TCT scores over time were both defined as primary endpoints. Secondary endpoints included length of stay and survival. A subgroup analysis for coronary artery bypass grafting (CABG) was performed. Results ACSM score increased during hospital stay (p p = 0.27), nor in the CABG subgroup (p = 0.15). The poster increased mobility to chair, toilet, corridor (all p p = 0.02) as measured by the activity-specific TCT scores, without differences in length of stay or survival. Conclusions ACSM score measured day-to-day functional changes, without significant differences between the poster mobilization and usual care group. Actual activities measured with the TCT score did improve. The mobilization poster is now new standard care, and effects in other centers and other departments should be assessed. Trial registration This study does not fall under the ICMJE trial definition and was not registered.

This paper considers optimal design of queueing networks in which each node consists of a single-server FIFO queue and an infinite-server queue, which is referred to as incubation queue. Upon service completion at a FIFO queue, a job splits (forks) into two parts: the first part is routed to the next node on its route, and the second part is placed in the incubation queue. Routing of the jobs of multiple types is governed by a central decision maker that decides on the routes for each job type and aims to minimize the mean turnaround time of the jobs, i.e., the time spent in the system until service completion at the FIFO queue in the last node, and at all incubation queues on the job's route, which may be viewed as a join operation. We provide explicit results for the turnaround time when all service and inter-arrival time distributions are exponential and invoke the Queueing Network Analyzer when these distributions are general. We then develop a Simulated Annealing approach to find the optimal routing configuration. We apply our approach to determine the optimal routing configuration in a chemistry analyzer line.

Teams are nowadays seen as the cornerstones of organizations. Previous research has shown that team reflexivity is positively related to team performance. Traditionally, team reflexivity is conceptualized as a process that occurs during transition moments, ignoring reflexive moments during teams’ action phases. Moreover, most studies used self-reported questionnaires and cross-sectional designs and thus provided limited insights into how team reflexivity unfolds during both the action and transition phases of teams. In this study, we adopt a social regulation perspective to develop an analytical framework to study team reflexivity in the flow of work. The study was conducted in a software development setting and included 50 h of video recordings of different types of team meetings of six professional self-managing teams (a total of 33 team members). Using concepts from social regulation theory as developed in student learning settings as an analytical lens, an analytical framework with four components of social regulation (knowledge co-construction and regulation; regulation activities; focus of regulation, and type of interaction) was developed and applied. Outcomes show that in more than half of their conversations, the teams jointly engaged in regulation-related activities, of which most concerned planning activities and a very low occurrence of evaluation activities. Different patterns of team reflexivity were found in the action and transition phase but zooming in on the interactions also showed high interrelatedness of the different activities. The analytical framework could assist future research to further study the interaction between the different components and how they mutually relate to team performance.

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

This reaction time study tested the hypothesis that in the case of finger movements skilled motor control involves the execution of learned hand postures. After delineating hypothetical control mechanisms and their predictions an experiment is described involving 32 participants who practiced 6 chord responses. These responses involved the simultaneous depression of one, two or three keys with either four right-hand fingers or two fingers of both hands. After practicing each of these responses for 240 trials, the participants performed the practiced and also novel chords with the familiar and with the unfamiliar hand configuration of the other practice group. The results suggest that participants learned hand postures rather than spatial or explicit chord representations. Participants practicing with both hands also developed a bimanual coordination skill. Chord execution was most likely slowed by interference between adjacent fingers. This interference seemed eliminated with practice for some chords but not for others. Hence, the results support the notion that skilled control of finger movements is based on learned hand postures that even after practice may be slowed by interference between adjacent fingers.

Managing the rapid growth of deprived areas (commonly known as slums, informal settlements, etc.) in cities of Low- to Middle-Income Countries (LMICs) demands detailed and consistent information that is often unavailable. Recent Earth Observation (EO) mapping approaches with supervised classification models overlook the diversity of deprived areas and require resource-intensive training sets. In this study, we analyse the potential of unsupervised machine learning (ML) models to capture intra-urban diversity of deprived areas in São Paulo, using solely open geodata. We provide a workflow of characterising deprivation at a city scale with a disaggregated approach, offering scalability and transferability potential. First, we extract a pool of spatial features from open geospatial datasets to characterise the morphological and environmental conditions of the study area. After input preparation, we train and optimise a k-means model, including a coupled feature importance tool. Four cluster types emerged with different deprivation aspects such as higher and lower accessibility to services and infrastructure, sparser and denser occupation; regular and complex morphology; flat and steep terrain. This alternative methodology to capture diversity of deprived areas with open EO-based features can inform locally targeted, thus more efficient, urban policies and interventions.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

In crisis management decision-making, decision-makers have to combine (limited) situational information with their own experience. Whereas traditional, analog training of decision-making situations in crisis management costs considerable time and effort, digital serious games can be used as more accessible training environments to offer additional training moments. Another advantage is that digital games offer new didactic opportunities, such as inducing specific reflection from the trainees. This study examines the effect of self-reflection through social comparison on information usage and information literacy of players of a digital serious game for crisis management decision-making training. In an experiment, data was collected from 73 participants, 47 were eligible for further analysis, split over two conditions. Participants played two gameplay scenarios in fixed order. Participants in the experimental condition, between the two scenarios, saw a dashboard displaying their own as well as previous players’ in-game behavior up to that point. Participants in the control condition received no intervention between playing the two scenarios. Overall, participants in the experimental condition used significantly more information from more different sources, and compared to the control condition they kept taking significantly more time to decide in the second scenario. No significant between-condition differences regarding information literacy were found. Results indicate that in-game behavior can be (positively) influenced by letting players self-reflect on their own in-game behavior through social comparison. Results also suggest that the dashboard should display more specific information of players’ in-game behavior, providing guidance on what to improve, rather than simply offering a broad overview.

This study sought to develop and evaluate an online module based on inquiry learning with digital laboratories, which was intended to address students’ misconceptions in a science domain. In a quasi-experimental design, 171 first-year students in a higher education introductory physics course on circular motion were as their existing groups assigned to an experimental (n = 100) or a control (n = 71) condition. The experimental condition was developed by arranging online inquiry activities that would encourage students to probe five identified misconceptions. The control condition required students to engage in online inquiry following the traditional syllabus outline. Students in both conditions used the same type of digital laboratory setup. The participants learned about the topic of circular motion and their knowledge was assessed. Results of the knowledge test revealed that the experimental condition geared towards addressing students’ misconceptions facilitated conceptual change more than the control condition.

The 15 min city (or “X-minute city” in general) concept aims to give people access to all essential services and daily needs (e.g., healthcare, education, etc.) within X minutes of active transportation, to improve transport equity, sustainability, and traffic safety. To date, there is a lack of methods and tools to assess to what degree cities currently, or after implementing policies, comply with the X-minute city concept. This research aims to develop a methodology for quantifying the X-minute city through a metric (CSx) that was developed based on an accessibility framework and tested for cycling mode in the Utrecht region in the Netherlands as a study area. Travel data from the Netherlands mobility panel were analysed to determine input characteristics of the metric, such as the weight of destination types. Standardized gravity-based 2-step floating catchment area (2SFCA) accessibility scores for all destination types were weighted and aggregated into a composite metric that shows relative scores as an X-minute city. The results of the analysis show that 100% of the population in the Utrecht region has access to at least one destination for all 9 destination types within 15 min, whereas this number reduces to 94% within a 10 min cycling threshold; indicating the status of Utrecht as a cycling city with cycling-friendly infrastructure. Furthermore, low-income groups do not have lower cycling accessibility to the services in the 15 min city in the study area, reinforcing the notion that cycling can be an effective solution to reduce transport inequalities. The developed metric can be used to assess cities on their way towards becoming X-minute city, prioritise neighbourhoods to develop, set quantifiable goals, and evaluate planning scenarios.

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.

Aim: Climate change is affecting biodiversity at an accelerating rate. Despite the importance of fungi in ecosystems in general, and in the global carbon and nitrogen cycle in particular, there is little research on the response of fungi to climate change compared with plants and animals. Earlier studies show that climatic factors and tree species are key determinants of macrofungal diversity and distribution at large spatial scales. However, our knowledge of how climate change will affect macrofungal diversity and distribution in the future remains poorly understood. Location: Europe. Methods: Using openly available occurrence data of 1845 macrofungal species from eight European countries (i.e. Norway, Sweden, Finland, Denmark, Netherlands, Germany, France and Spain), we built ensemble species distribution models to predict macrofungal response to climate change alone and combined climate and tree distribution change under the IPCC special report on 2080 emissions scenarios (SRES A2 and B2). Results: Considering climate change alone, we predict that about 77% (74.1%–80.7%) of the modelled species will expand their distribution range, and around 57% (56.1%–58.4%) of the modelled area will have an increase in macrofungal species richness. However, when considering the combined climate and tree species distribution change, only 50% (50%–50.9%) of the species are predicted to expand their distribution range and 49% (47.4%–51.1%) of the modelled area will experience an increase in macrofungal species richness. Main Conclusions: Overall, our models projected that large areas would exhibit increased macrofungal species richness under future climate change. However, tree species distribution might play a restrictive role in the future distributional shifts of macrofungi. In addition, macrofungal responses appear heterogeneous, varying among species and regions. Our findings highlight the importance of including tree species in the projection of climate change impacts on the macrofungal diversity and distribution on a continental scale.

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.

Background: An accurate estimation of the glenohumeral joint center of rotation (CoR) is important during alignment of braces and exoskeletons, as a misalignment will introduce undesired forces on the human body. The aim of this research was to develop a new method to estimate the glenohumeral CoR and register the location to the body using a single camera and two printed markers. Methods: During shoulder anteflexion, the arm roughly describes an arc in the sagittal plane, with the glenohumeral joint in the center. Two binary square-fiducial ArUco markers were secured to the upper arm and the scapula, their position and orientation were obtained, and a sphere was fitted to the coordinates of the arm marker. The sphere center position was then registered on the skin. The accuracy was assessed with a test bench with a known rotational center. The repeatability was assessed in vivo with five healthy participants. Results: The mean absolute offset between the true CoR of the test bench and the fitted sphere centers across multiple trials was 2.7 mm at a velocity of 30 degrees/s, and 2.5 mm at 60 degrees/s. The root mean squared distance from the estimated sphere centers after each trial to the mean sphere center across all trials per participant was 5.1 mm on average for the novice examiner and 5.2 mm for the expert examiner. Conclusions: The proposed method is able to accurately and precisely estimate the glenohumeral CoR.

Objective: We aim to provide a quantitative description of the relation between seizure duration and the postictal state using features extracted from the postictal electroencephalogram (EEG). Methods: Thirty patients with major depressive disorder treated with electroconvulsive therapy (ECT) were studied with continuous EEG before, during, and after ECT-induced seizures. EEG recovery was quantified as the spectral difference between postictal and baseline EEG using the temporal brain symmetry index (BSI). The postictal temporal EEG evolution was modeled with a single exponential. The parameters of the model, including the time constant τ, describe the change and speed of postictal EEG recovery. The change from baseline EEG at t = 60 minutes post-seizure (ΔBSI) was calculated from the exponential fit. Postictal clinical reorientation time (ROT) was clinically established. A multivariate generalized multi-level Bayesian model was estimated with seizure duration and ROT as predictors of τ and ΔBSI. Results: EEG features of 290 seizures and postictal states were used for analyses. The model faithfully described the dynamics of the postictal EEG in nearly all patients. Seizure duration was associated with the recovery time constant, τ, and ΔBSI. ROT was associated with τ, but not with ΔBSI. Conclusions: Longer seizures are associated with slower postictal EEG recovery and more enduring EEG changes compared to baseline. Significance: Quantitative EEG allows objective assessment of the postictal state.

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.

The availability of sensors, eye-trackers, smartwatches, Wi-Fi trackers, or other digital devices facilitates the collection of new types of data that can be used for measurement. The question is how to analyze them. Several psychometric models are available, but even though they have been applied successfully in many testing programs, they do have their limits with respect to the kind of data they can be applied to. Artificial intelligence (AI) offers many methods for dealing with these new and more complex datasets. They do have some limitations when it comes to reliable and valid measurement thought. The question arises how to apply them in the field of psychometrics. To answer this question, the field of psychometrics is introduced first. Besides, the benefits and disadvantages of artificial intelligence are illustrated in three examples. A promising development, when it comes to the application of AI in the field of psychometrics, is referred to as trustworthy AI (TAI), with principles related to fairness, explainability, and accountability. Based on the examples of the use of AI in social and health science and the lessons learned from the approaches to integrate new data types in existing psychometric models, a framework is defined with nine steps for the use of AI in psychometrics. For each of these steps, it is evaluated how TAI can be applied for reliable and valid measurement. The chapter concludes with the observation that straightforward application of AI in the field of Psychometrics might still be a step too far, but that the developments related to TAI go fast and offer new and exciting opportunities for the application of AI to psychometrics.

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.

Traditional acquisition of height data to generate normalized digital surface models (nDSMs) of very high spatial resolution is time-consuming and expensive. Height estimation by means of optical remote sensing images is a more efficient and timely way to do so. Recent studies employed supervised learning methods. State-of-the-art computer vision methods, however, overlook semantic consistency during remote sensing image translation and neglect multi-task correlations for specific task learning. To address these problems, this paper proposes a semantic-aware unsupervised domain adaptation method for height estimation. The method consists of image translation and multitask representation learning for height estimation. We tested the transferability of our method from the ISPRS Postdam data set to the Vaihingen data set and a custom dataset of Enschede. In the image translation task, our method improved the Fréchet Inception Distance (FID) metric by at least 12.8% and 12.1% on the two datasets, respectively. In the height estimation task, our method achieved RMSEs of 3.257 m and 3.875 m, which are at least 0.603 m and 0.072 m lower than the compared unsupervised algorithm, and achieved competitive results with the supervised learning algorithm. Our results show the advantages of the proposed method in height estimation and image translation as compared to alternative strategies. We conclude that adding semantic supervision improves height estimation from single-view orthophoto under unsupervised domain adaptation. It also alleviates the problem of limited access to nDSM data for training the method.

Clean floors in dairy farms are of vital importance to mitigate risks regarding cow welfare and to avoid ammonia emissions. In modern dairy farms it is common to deploy manure cleaning robots to automate the cleaning task. In the current state-of-the art, robots operate in relative solitude with no coordination or cooperation within the fleet. This paper employs discrete-event simulation to test the effectiveness of different strategies for cooperative, team-based cleaning in dairy farms. Special attention is paid to the impact of various team compositions and robot characteristics on team routing. The results look promising: the minimum cleanliness can be increased by deploying teams while simultaneously reducing the number of cow-robot interactions.

The use of empirical prior information about participants has been shown to substantially improve the efficiency of computerized adaptive tests (CATs) in educational settings. However, it is unclear how these results translate to clinical settings, where small item banks with highly informative polytomous items often lead to very short CATs. We explored the risks and rewards of using prior information in CAT in two simulation studies, rooted in applied clinical examples. In the first simulation, prior precision and bias in the prior location were manipulated independently. Our results show that a precise personalized prior can meaningfully increase CAT efficiency. However, this reward comes with the potential risk of overconfidence in wrong empirical information (i.e., using a precise severely biased prior), which can lead to unnecessarily long tests, or severely biased estimates. The latter risk can be mitigated by setting a minimum number of items that are to be administered during the CAT, or by setting a less precise prior; be it at the expense of canceling out any efficiency gains. The second simulation, with more realistic bias and precision combinations in the empirical prior, places the prevalence of the potential risks in context. With similar estimation bias, an empirical prior reduced CAT test length, compared to a standard normal prior, in 68% of cases, by a median of 20%; while test length increased in only 3% of cases. The use of prior information in CAT seems to be a feasible and simple method to reduce test burden for patients and clinical practitioners alike.

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.

Maps, like other types of extensive data collections, are usually created and maintained by a larger number of individuals. The number of individuals using the map is even larger in most cases. Considering the complex interaction of these people, the question arises as to why maps can be used meaningfully. Ultimately, the represented geographical reality can rarely be perfectly reconstructed from the map, and misunderstandings are inevitable when using the map. This article sets factors into context that facilitate the readability of a map as well as factors that can lead to misunderstandings and non-interpretability. The creation of a map is thereby considered a complex system the stability, coherence, and heterogeneity of which can be explained by its attractors and, in the temporal context, by means of disruptive behavior and autopoiesis. To this end, a coherence theory of map making and reading is proposed. This allows for a broader perspective on the map-making process and a deeper understanding of a map’s affordances. In particular, the considerations made can serve as a starting point to develop better measures of data quality and fitness for purpose. Finally, a more reflective behavior and active influence on the map-making process is made possible.

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.

Introduction Only 5% to 15% of individuals with amputation living in low- and middle-income countries (LMICs) have access to proper prostheses. Mainly, prosthetic costs are too high, and facilities are not within reach. Measurement and production of traditional prosthetic sockets are time-consuming, labor-intensive, and highly dependent on the experience and skills of the personnel involved. Materials and Methods This report describes the workflow to produce low-cost patella tendon bearing transtibial prosthetic sockets. Using computer-aided design (CAD) and computer-aided manufacturing (CAM), transtibial prostheses can be easily produced in rural areas. The size of the residual limb was scanned with a handheld 3D-scanner (Einscanner Pro Plus), and the sockets were printed using fused filament fabrication (FFF) with an Ultimaker S5. The foot was made locally, and the other prosthetic parts were imported. The 3D-printed socket costs US $20 (excluding value-added tax [VAT]). The total material cost of the prosthesis, including the other prosthetic materials, amounts to approximately US $100 (excluding VAT). Assuming the asset cost of the devices, the costs of one local employee, overhead expenses, a profit margin, and the VAT included, a 3D-printed prosthesis could be sold for US $170. Conclusions This report provides a blueprint to produce low-cost 3D-printed transtibial prosthetic sockets. Further research will be conducted to replace the imported prosthetic parts for local products and to automatize the digital design process. Clinical Relevance With this workflow, prosthetic sockets can be produced consistently, which makes it a suitable method in LMICs.

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.

Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this research aims to integrate Remote Sensing Imagery (RSI) and Street View Images (SVI) for slum mapping. Jakarta city is the study area representing the challenge of distinguishing between slum and non-slum kampungs, and these kampungs accommodate approximately 60% of the population of Jakarta. This research compares the mapping results obtained by four DL networks: FCN-DK6 used only RSI, a VGG16 used only SVI, and two networks combined RSI and SVI (FCN-DK6-i and Modified FCN-DK6). Further, the Modified FCN-DK6 network was explored by integrating SVI at each convolutional layer, i.e., Modified FCN-DK6_1, Modified FCN-DK6_2, Modified FCN-DK6_3, Modified FCN-DK6_4, and Modified FCN-DK6_5. Experimental results demonstrate that combining RSI and SVI improves the accuracy, depending on how and at what level in the FCN network they are integrated. The Modified FCN-DK6_2 outperforms the rest in Modified FCN-DK6 experiments and FCN-DK6-i.

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.

During the COVID-19 pandemic, a 13.6 per cent reduction in the number of surgical procedures performed was observed in 2020. Despite great pressure on healthcare, the COVID-19 pandemic did not cause an increase in adverse surgical outcomes, and oncological surgery-related duration of hospital and ICU stay were significantly shorter.Background The COVID-19 pandemic caused disruption of regular healthcare leading to reduced hospital attendances, repurposing of surgical facilities, and cancellation of cancer screening programmes. This study aimed to determine the impact of COVID-19 on surgical care in the Netherlands. Methods A nationwide study was conducted in collaboration with the Dutch Institute for Clinical Auditing. Eight surgical audits were expanded with items regarding alterations in scheduling and treatment plans. Data on procedures performed in 2020 were compared with those from a historical cohort (2018-2019). Endpoints included total numbers of procedures performed and altered treatment plans. Secondary endpoints included complication, readmission, and mortality rates. Results Some 12 154 procedures were performed in participating hospitals in 2020, representing a decrease of 13.6 per cent compared with 2018-2019. The largest reduction (29.2 per cent) was for non-cancer procedures during the first COVID-19 wave. Surgical treatment was postponed for 9.6 per cent of patients. Alterations in surgical treatment plans were observed in 1.7 per cent. Time from diagnosis to surgery decreased (to 28 days in 2020, from 34 days in 2019 and 36 days in 2018; P < 0.001). For cancer-related procedures, duration of hospital stay decreased (5 versus 6 days; P < 0.001). Audit-specific complications, readmission, and mortality rates were unchanged, but ICU admissions decreased (16.5 versus 16.8 per cent; P < 0.001). Conclusion The reduction in the number of surgical operations was greatest for those without cancer. Where surgery was undertaken, it appeared to be delivered safely, with similar complication and mortality rates, fewer admissions to ICU, and a shorter hospital stay.Lay Summary COVID-19 has had a significant impact on healthcare worldwide. Hospital visits were reduced, operating facilities were used for COVID-19 care, and cancer screening programmes were cancelled. This study describes the impact of the COVID-19 pandemic on Dutch surgical healthcare in 2020. Patterns of care in terms of changed or delayed treatment are described for patients who had surgery in 2020, compared with those who had surgery in 2018-2019. The study found that mainly non-cancer surgical treatments were cancelled during months with high COVID-19 rates. Outcomes for patients undergoing surgery were similar but with fewer ICU admissions and shorter hospital stay. These data provide no insight into the burden endured by patients who had postponed or cancelled operations.

Purpose Visual assessment of Rubidium (Rb-82) PET myocardial perfusion images is usually combined with global myocardial flow reserve (MFR) measurements. However, small regional blood flow deficits may go unnoticed. Our aim was to compare the diagnostic value of regional with global MFR in the detection of obstructive coronary artery disease (oCAD). Methods We retrospectively included 1519 patients referred for rest and regadenoson-induced stress Rb-82 PET/CT without prior history of oCAD. MFR was determined globally, per vessel territory and per myocardial segment and compared using receiver-operating characteristic analysis. Vessel MFR was defined as the lowest MFR of the coronary territories and segmental MFR as the lowest MFR of the 17-segments. The primary endpoint was oCAD on invasive coronary angiography. Results The 148 patients classified as having oCAD had a lower global MFR (median 1.9, interquartile range [1.5–2.4] vs. 2.4 [2.0–2.9]), lower vessel MFR (1.6 [1.2–2.1] vs. 2.2 [1.9–2.6]) and lower segmental MFR (1.3 [ 0.9–1.6] vs. 1.8 [1.5–2.2]) as compared to the non-oCAD patients (p Conclusions The use of regional MFR instead of global MFR is recommended as it improves the diagnostic value of Rb-82 PET in the detection of oCAD.

Modern immersive multisensory communication systems can provide compelling mediated social communication experiences that approach face-to-facecommunication. Existing methods to assess the quality of mediated social communication experiences are typically targeted at specific tasks or communication technologies. As a result, they do not address all relevant aspects of social presence (i.e., the feeling of being in the presence of, and having an affective and intellectual connection with, other persons). Also, they are typically unsuitable for application to social communication in virtual (VR), augmented (AR), or mixed (MR) reality. We propose a comprehensive, general, and holistic multi-scale (questionnaire-based) approach, based on an established conceptual framework for multisensory perception, to measure the quality of mediated social communication experiences. Our holistic approach to mediated social communication (H-MSC) assessment comprises both the experience of Spatial Presence (i.e., the perceived fidelity, internal and external plausibility, and cognitive, reasoning, and behavioral affordances of an environment) and the experience of Social Presence (i.e., perceived mutual proximity, intimacy, credibility, reasoning, and behavior of the communication partners). Since social presence is inherently bidirectional (involving a sense of mutual awareness) the multiscale approach measures both the internal (‘own’) and external (‘the other’) assessment perspectives. We also suggest how an associated multiscale questionnaire (the Holistic Mediated Social Communication Questionnaire or H-MSC-Q) could be formulated in an efficient and parsimonious way, using only a single item to tap into each of the relevant processing levels in the human brain: sensory, emotional, cognitive, reasoning, and behavioral. The H-MSC-Q can be sufficiently general to measure social presence experienced with any (including VR, AR, and MR) multi-sensory (visual, auditory, haptic, and olfactory) mediated communication system. Preliminary validation studies confirm the content and face validity of the H-MSC-Q. In this paper, we focus on the underlying concepts of the H-MSC-Q. We make the initial draft questionnaire available to the community for further review, development, and validation. We hope it may contribute to the unification of quality measures for mediated social communication.

Outsourcing emerging technologies such as Artificial Intelligence (AI) are expected to impact organisations significantly, due to a tight labour market for AI expertise. However, how formal and relational governance effects Information Systems suppliers who provide AI services has not been studied. Based on an exploratory research amongst eight suppliers and two market research advisors, we conducted 18 expert interviews and found evidence how formal contractual and relational governance affects AI outsourcing. The results indicate various forms of contractual models in which some cater for clients’ needs specifically, e.g. outcome-based, experience-driven Service Level Agreements (SLAs). Our examination provides insights that formal and relational Information Systems (IS) outsourcing governance are complementary in cases where clients and suppliers co-develop AI. For our research, we adapted the outsourcing governance model of Lioliou et al. (Inf Syst J 24:503–535, 2014), including their emphasis on the psychological contract. We contribute to IS outsourcing literature by exploring, beyond contract management insights, differences between suppliers in providing AI services. Our study acknowledges that AI outsourcing shift the emphasis from a transactional type of arrangement to a relational type of outsourcing arrangement. In addition, the combination of both formal and relational governance mechanisms positively contributes to IS governance. Our study also confirms that the innovative character of AI positively contributes to the psychological contract in outsourcing AI.

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.

Aim:This study aims to compare the gating signals of patients with lung cancer recorded during the planning computed tomography scan with the ones recorded during treatment fractions. The results provide insights into how representative the respiratory signals from the planning scan are for radiation dose partitioning.Materials and methods:The amplitude and frequency of the respiratory signals of 29 patients with lung carcinoma were analysed and compared with the amplitude and frequency of those recorded during their planning scans. Moreover, a cross-correlation analysis was performed between the difference between the planning scan and fractions and features from the planning scan.Results:Two patients showed significantly different amplitude and frequency during treatment fractions compared to those from the planning scan. These patients showed low variances in frequency and amplitude during the different fractions. The difference between planning scan and fractions is correlated with the variances within the planning scan.Findings:Respiratory signals can differ between the planning scan and the fractions. In this case, a new planning scan may be beneficial. The respiratory signals from the planning scan may be predictive of whether the planning scan will be representative and usable as a control measure during radiotherapy fractions.

In this article, a theoretical model on the cumulation of noise, generated by large numbers of man-made noise (MMN) sources in several types of residential environments, has been developed. The model is based on extensive propagation measurements and verified by earlier field strength measurements. From applying the model it appears that MMN sources, emitting powers that equals mains port conductive disturbance limits in the electromagnetic compatibility standards, do not explain the measured noise field strength values. In contrary, the model indicates that the average source power is significant higher than can be derived from the limit in the standards. That could mean that the average power at the mains port is substantial higher than what generally is assumed. But alternately, the power, delivered by other ports like the ports for dc power, for telecom and for other wiring, is much higher than thought and could indicate a disregarded aspect in the electromagnetic compatibility (EMC) standards.

Multispectral pedestrian detection has received much attention in recent years due to its superiority in detecting targets under adverse lighting/weather conditions. In this paper, we aim to generate highly discriminative multi-modal features by aggregating the human-related clues based on all available samples presented in multispectral images. To this end, we present a novel multispectral pedestrian detector performing locality guided cross-modal feature aggregation and pixel-level detection fusion. Given a number of single bounding boxes covering pedestrians in both modalities, we deploy two segmentation sub-branches to predict the existence of pedestrians on visible and thermal channels. By referring to the important locality information in the reference modality, we perform locality guided cross-modal feature aggregation to learn highly discriminative human-related features in the complementary modality by exploring the clues of all available pedestrians. Moreover, we utilize the obtained spatial locality maps to provide prediction confidence scores in visible and thermal channels and conduct pixel-wise adaptive fusion of detection results in complementary modalities. Extensive experiments demonstrate the effectiveness of our proposed method, outperforming the current state-of-the-art detectors on both KAIST and CVC-14 multispectral pedestrian detection datasets.

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.

At some point during transport, intermodal containers will be stored at a terminal, where they are typically stacked on top of each other. Stacking yields a higher utilization of the area but may lead to unproductive reshuffle moves when containers below another need to be retrieved. Preventing reshuffles has a financial benefit, as it not only avoids the costs of executing the reshuffle but also decreases the time needed to retrieve a container. Typically, researchers consider only the retrieval of containers and assume the retrieval order is fully known. In addition, existing studies do not consider the stacking restrictions imposed by a reach stacker, which is commonly used in smaller inland terminals. This research aims to design decision support for determining real-life applicable container stack allocations so that the expected number of reshuffles is minimized. We propose a model that includes both arrivals and departures of containers as well as a certain level of uncertainty in the order thereof. The problem is modeled as a Markov Decision Process and solved using Approximate Dynamic Programming (ADP). Through numerical experiments on real-life problem instances, we conclude that the ADP approach drastically outperforms a benchmark heuristic from the literature. All data used as well as the source code has been made publicly available.

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.

As the Internet of Things (IoT) is making its entrance in people’s homes, differences in the skills to operate smart home devices need to be considered. This study examined (1) the levels of digital skills to use IoT home automation among Dutch adult citizens and (2) differences of these skills over gender, age, and education. Therefore, a performance test with actual real-life tasks was conducted among a representative sample ( N = 99) of the Dutch adult population to measure digital skill levels. The participants performed tasks while using interconnected smart home devices in a virtual test environment. The results revealed that the Dutch adult population possesses insufficient data and strategic skills to use smart home devices to its full potential. Even less likely to benefit are the elderly and less educated; they showed the lowest levels of data and strategic skills. In addition, the elderly lack operational skills to use IoT home automation beneficially.