Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D data capture, thus providing a much larger coverage within shorter acquisition time. This paper compares and discusses advantages and disadvantages of multi-temporal MLS data acquisition compared to established TLS data recording schemes. In this pilot study on integrated TLS and MLS data processing in a forest, it could be shown that existing TLS data evaluation routines can be used for MLS data processing. Methods of automatic laser scanner data processing for forest inventory parameter determination and quantitative structure model (QSM) generation were tested in two sample plots using data from both scanning methods and from different seasons. TLS in a multi-scan configuration delivers very high-density 3D point clouds, which form a valuable basis for generating high-quality QSMs. The pilot study shows that MLS is able to provide high-quality data for an equivalent determination of relevant forest inventory parameters compared to TLS. Parameters such as tree position, diameter at breast height (DBH) or tree height can be determined from MLS data with an accuracy similar to the accuracy of the parameter derived from TLS data. Results for instance in DBH determination by cylinder fitting yielded a standard deviation of 1.1 cm for trees in TLS data and 3.7 cm in MLS data. However, the resolution of MLS scans was found insufficient for successful QSM generation. The registration of MLS data in forests furthermore requires additional effort in considering effects caused by poor GNSS signal.

In this empirical research, the author focuses on the enterprise resource planning (ERP) software market. Based on a contingent perspective of how markets emerge and can be shaped, the author asks the research question of whether the emergence of the ERP market was a necessary, strong, or weak consequence of the product innovation of Systems, Applications & Products in Data Processing (SAP), which was the pioneer innovator in this specific market. This question is answered with a graph theoretical model of contingency and causality in order to measure the causality between events occurring over historical time. In this sense, the research article provides an application of the method proposed by Lehmann-Waffenschmidt. The author finds that the emergence of the ERP software market is contingent and was not predetermined; path dependencies play a big role in the way how this market segment emerged. With respect to both entrepreneurial and economic factors of relevance, the case of the SAP is far from a predetermined success story. The results are relevant for a number of reasons. First, the results indicate that instead of talking about success stories, a new perspective in market shaping can highlight a more realistic way of the contingent nature of entrepreneurial activity and product innovations. Second, the results aim to bridge the gap between marketing and the emergence of markets, as it was indicated as a research gap by recent contributions in marketing science. Third, the introduction of counterfactual events in the business history of SAP indicate a methodological innovation that has not yet been considered by marketing and entrepreneurship scholars, which may be helpful regarding recognizing patterns from the past and also regarding contingent planning for the future.

People are better able to empathize with others when they are given information concerning the context driving that person’s experiences. This suggests that people draw on prior memories when empathizing, but the mechanisms underlying this connection remain largely unexplored. The present study investigates how variations in episodic information shape the emotional response towards a movie character. Episodic information is either absent or provided by a written context preceding empathic film clips. It was shown that sad context information increases empathic concern for a movie character. This was tracked by neural activity in the temporal pole (TP) and anterior hippocampus (aHP). Dynamic causal modeling with Bayesian Model Selection has shown that context changes the effective connectivity from left aHP to the right TP. The same crossed-hemispheric coupling was found during rest, when people are left to their own thoughts. We conclude that (i) that the integration of episodic memory also supports the specific case of integrating context into empathic judgments, (ii) the right TP supports emotion processing by integrating episodic memory into empathic inferences, and (iii) lateral integration is a key process for episodic simulation during rest and during task. We propose that a disruption of the mechanism may underlie empathy deficits in clinical conditions, such as autism spectrum disorder.

Basic facial emotion recognition is suggested to be negatively affected by puberty onset reflected in a “pubertal dip” in performance compared to pre- or post-puberty. However, findings remain inconclusive. Further, research points to an own-age bias, i.e., a superior emotion recognition for peer faces. We explored adolescents’ ability to recognize specific emotions. Ninety-five children and adolescents, aged 8–17 years, judged whether the emotions displayed by adolescent or adult faces were angry, sad, neutral, or happy. We assessed participants a priori by pubertal status while controlling for age. Results indicated no “pubertal dip”, but decreasing reaction times across adolescence. No own-age bias was found. Taken together, basic facial emotion recognition does not seem to be disrupted during puberty as compared to pre- and post-puberty.

Forward osmosis (FO) is a membrane technology that uses the osmotic pressure difference to treat two fluids at a time giving the opportunity for an energy-efficient water and wastewater treatment. Various applications are possible; one of them is the application in industrial water management. In this review paper, the basic principle of FO is explained and the state-of-the-art regarding FO application in manufacturing industries is described. Examples of FO application were found for food and beverage industry, chemical industry, pharmaceutical industry, coal processing, micro algae cultivation, textile industry, pulp and paper industry, electronic industry, and car manufacturing. FO publications were also found about heavy metal elimination and cooling water treatment. However, so far FO was applied in lab-scale experiments only. The up-scaling on pilot- or full-scale will be the essential next step. Long-term fouling behavior, membrane cleaning methods, and operation procedures are essential points that need to be further investigated. Moreover, energetic and economic evaluations need to be performed before full-scale FO can be implemented in industries.

Soil spectra are often measured in the laboratory, and there is an increasing number of large-scale soil spectral libraries establishing across the world. However, calibration models developed from soil libraries are difficult to apply to spectral data acquired from the field or space. Transfer learning has the potential to bridge the gap and make the calibration model transferrable from one sensor to another. The objective of this study is to explore the potential of transfer learning for soil spectroscopy and its performance on soil clay content estimation using hyperspectral data. First, a one-dimensional convolutional neural network (1D-CNN) is used on Land Use/Land Cover Area Frame Survey (LUCAS) mineral soils. To evaluate whether the pre-trained 1D-CNN model was transferrable, LUCAS organic soils were used to fine-tune and validate the model. The fine-tuned model achieved a good accuracy (coefficient of determination (R²) = 0.756, root-mean-square error (RMSE)= 7.07 and ratio of percent deviation (RPD) = 2.26) for the estimation of clay content. Spectral index, as suggested as a simple transferrable feature, was also explored on LUCAS data, but did not performed well on the estimation of clay content. Then, the pre-trained 1D-CNN model was further fine-tuned by field samples collect in the study area with spectra extracted from HyMap imagery, achieved an accuracy of R² = 0.601, RMSE = 8.62 and RPD = 1.54. Finally, the soil clay map was generated with the fine-tuned 1D-CNN model and hyperspectral data.

Longitudinal monitoring of BCR-ABL transcript levels in peripheral blood of CML patients treated with tyrosine kinase inhibitors (TKI) revealed a typical biphasic response. Although second generation TKIs like dasatinib proved more efficient in achieving molecular remission compared to first generation TKI imatinib, it is unclear how individual responses differ between the drugs and whether mechanisms of drug action can be deduced from the dynamic data. We use time courses from the DASISION trial to address statistical differences in the dynamic response between first line imatinib vs. dasatinib treatment cohorts and we analyze differences between the cohorts by fitting an established mathematical model of functional CML treatment to individual time courses. On average, dasatinib-treated patients show a steeper initial response, while the long-term response only marginally differed between the treatments. Supplementing each patient time course with a corresponding confidence region, we illustrate the consequences of the uncertainty estimate for the underlying mechanisms of CML remission. Our model suggests that the observed BCR-ABL dynamics may result from different, underlying stem cell dynamics. These results illustrate that the perception and description of CML treatment response as a dynamic process on the level of individual patients is a prerequisite for reliable patient-specific response predictions and treatment optimizations.

Implant dentistry is a growing field in the education of undergraduate dental students. The present laboratory study evaluates factors which may potentially influence the accuracy of free-hand implant insertion and the use of an orientation template. After three-dimensional planning using coDiagnostiXTM, orientation templates, including sleeves for the pilot-drill in regions 41 and 45, were manufactured by thermoforming. Sixty-one fifth year dental students inserted one implant using the orientation template and another implant free-hand in an artificial mandible. Information regarding age, sex, handedness, education, and the time required for implant insertion were recorded. Subsequently, the mandibles were scanned using cone-beam-computed tomography and the accuracy of the implant position was assessed, while statistical analysis followed. The free-hand implant insertion resulted in a distal deviation of −1.34 ± 5.15° and a mesial mismatch of 0.06 ± 0.79 mm at the artificial bone level compared to the sleeves. When using the orientation templates, the deviation decreased to −0.67 ± 3.48° and a distal mismatch of −0.22 ± 0.62 mm was achieved. The difference was statistically significant for the mismatch (p < 0.049). Regarding the limitations of our study, it could be said that the accuracy level achieved by dental undergraduates using implant placement with orientation templates is comparable to that in other studies.

Background The detection of early warning signs is essential in the long-term treatment of bipolar disorders. However, in bipolar patients’ daily life and outpatient treatment the assessment of upcoming state changes faces several difficulties. In this trial, we examine the effectiveness of a smartphone based automated feedback about ambulatory assessed early warning signs in prolonging states of euthymia and therefore preventing hospitalization. This study aims to assess, whether patients experience longer episodes of euthymia, when their treating psychiatrists receive automated feedback about changes in communication and activity. With this additional information an intervention at an earlier stage in the development of mania or depression could be facilitated. We expect that the amount of time will be longer between affective episodes in the intervention group. Methods/design The current study is designed as a randomized, multi-center, observer-blind, active-control, parallel group trial within a nationwide research project on the topic of innovative methods for diagnostics, prevention and interventions of bipolar disorders. One hundred and twenty patients with bipolar disorder will be randomly assigned to (1) the experimental group with included automated feedback or (2) the control group without feedback. During the intervention phase, the psychopathologic state of all participants is assessed every four weeks over 18 months. Kaplan-Meier estimators will be used for estimating the survival functions, a Log-Rank test will be used to formally compare time to a new episode across treatment groups. An intention-to-treat analysis will include data from all randomized patients. Discussion This article describes the design of a clinical trial investigating the effectiveness of a smartphone-based feedback loop. This feedback loop is meant to elicit early interventions at the detection of warning signs for the prevention of affective episodes in bipolar patients. This approach will hopefully improve the chances of a timely intervention helping patients to keep a balanced mood for longer periods of time. In detail, if our hypothesis can be confirmed, clinical practice treating psychiatrists will be enabled to react quickly when changes are automatically detected. Therefore, outpatients would receive an even more individually tailored treatment concerning time and frequency of doctor’s appointments. Trial registration: ClinicalTrials.gov: NCT02782910: Title: “Smartphone-based Ambulatory Assessment of Early Warning Signs (BipoLife_A3)”. Registered May 25 2016. Protocol Amendment Number: 03. Issue Date: 26 March 2018. Author(s): ES.

Abstract Background: The world population is aging and the number of older adults with bipolar disorder is increasing. Digital technologies are viewed as a framework to improve care of older adults with bipolar disorder. This analysis quantifes Internet use by older adults with bipolar disorder as part of a larger survey project about information seeking. Methods: A paper-based survey about information seeking by patients with bipolar disorder was developed and translated into 12 languages. The survey was anonymous and completed between March 2014 and January 2016 by 1222 patients in 17 countries. All patients were diagnosed by a psychiatrist. General estimating equations were used to account for correlated data. Results: Overall, 47% of older adults (age 60 years or older) used the Internet versus 87% of younger adults (less than 60 years). More education and having symptoms that interfered with regular activities increased the odds of using the Internet, while being age 60 years or older decreased the odds. Data from 187 older adults and 1021 younger adults were included in the analysis excluding missing values. Conclusions: Older adults with bipolar disorder use the Internet much less frequently than younger adults. Many older adults do not use the Internet, and technology tools are suitable for some but not all older adults. As more health services are only available online, and more digital tools are developed, there is concern about growing health disparities based on age. Mental health experts should participate in determining the appropriate role for digital tools for older adults with bipolar disorder.