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1. Recent reports on insect decline have highlighted the need for long-term data on insect communities towards identifying their trends and drivers.
2. With the launch of many new insect monitoring schemes to investigate insect communities over large spatial and temporal scales, Malaise traps have become one of the most important tools due to the broad spectrum of species collected and reduced capture bias through passive sampling of insects day and night. However, Malaise traps can vary in size, shape, and colour, and it is unknown how these differences affect biomass, species richness, and composition of trap catch, making it difficult to compare results between studies.
3. We compared five Malaise trap types (three variations of the Townes and two variations of the Bartak Malaise trap) to determine their effects on biomass and species richness as identified by metabarcoding.
4. Insect biomass varied by 20%–55%, not strictly following trap size but varying with trap type. Total species richness was 20%–38% higher in the three Townes trap models compared to the Bartak traps. Bartak traps captured lower richness of highly mobile taxa but increased richness of ground-dwelling taxa. The white roofed Townes trap captured a higher richness of pollinators.
5. We find that biomass, total richness, and taxa group specific richness are all sensitive to Malaise trap type. Trap type should be carefully considered and aligned to match monitoring and research questions. Additionally, our estimates of trap type effects can be used to adjust results to facilitate comparisons across studies.
Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
Background: Improving movement control might be a promising treatment goal during chronic non-specific low back pain (CLBP) rehabilitation. The objective of the study is to evaluate the effect of a single bout of game-based real-time feedback intervention on trunk movement in patients with CLBP.
Methods: Thirteen CLBP patients (8female;41 ± 16 years;173 ± 10 cm;78 ± 22 kg) were included in this randomized cross-over pilot trial. During one laboratory session (2 h), participants performed three identical measurements on trunk movement all including: first, maximum angle of lateral flexion was assessed. Secondly, a target trunk lateral flexion (angle: 20°) was performed. Main outcome was maximum angle ([°]; MA). Secondary outcomes were deviation [°] from the target angle (angle reproduction; AR) and MA of the secondary movement planes (rotation; extension/flexion) during lateral flexion. The outcomes were assessed by an optical 3D-motion-capture-system (2-segment-trunk-model). The measurements were separated by 12-min of intervention and/or resting (randomly). The intervention involved a sensor-based trunk exergame (guiding an avatar through virtual worlds). After carryover effect-analysis, pre-to-post intervention data were pooled between the two sequences followed by analyses of variances (paired t-test).
Results: No significant change from pre to post intervention for MA or AR for any segment occurred for the main movement plane, lateral flexion (p > .05). The upper trunk segment showed a significant decrease of the MA for trunk extension/flexion from pre to post intervention ((4.4° ± 4.4° (95% CI 7.06–1.75)/3.5° ± 1.29° (95% CI 6.22–0.80); p = 0.02, d = 0.20).
Conclusions: A single bout of game-based real-time feedback intervention lead to changes in the secondary movement planes indicating reduced evasive motion during trunk movement.
Global vernetzte Supply Chains (SC) führen bei den Unternehmen zu geringeren Kosten, aber zugleich erhöhen diese auch die Abhängigkeit ggü. den Lieferanten und die Störanfälligkeit der SCs wird erhöht. In den vergangenen Jahren haben zudem die Unsicherheiten für die SCs stark zugenommen. Treiber waren hier u.a. der Brexit, Handelskonflikte oder auch die Corona-Pandemie. In diesem Zusammenhang steht verstärkt die Entwicklung neuer SC-Strategien im Fokus der Unternehmen. Dabei wird stark auf die Verbesserung der Resilienz der SCs geachtet, um dadurch u.a. die Risiken für die SCs zu reduzieren. Die Arbeit beschäftigt sich mit den Auswirkungen steigender Unsicherheiten auf das Design sowie die Resilienz von SCs und hat das Ziel zu ermitteln, ob es Änderungen in der SC bedarf, um auf die Auswirkungen steigender Unsicherheiten zu reagieren und wie die Resilienz zukünftig sichergestellt werden kann (Trade-off zwischen Resilienz und Kosteneffizienz). Im Rahmen der Untersuchung erfolgte eine qualitative Forschung in Form von Experteninterviews, da so u.a. spezifische Meinungen oder auch Begründungen und Einstellungen von bestimmten Personen zu den vorliegenden Thematiken ermittelt werden können. Die Ergebnisse zeigen, dass die Kosten weiterhin meist der entscheidende Aspekt sind und es mehr Transparenz, Flexibilität sowie ein besseres Risikomanagement nötig ist. Des Weiteren bedarf es zukünftig u.a. einer größeren Berücksichtigung von Unsicherheiten, eine Erhöhung von Sicherheitsbeständen sowie eine Verringerung der Komplexität der SCs und u.U. mehr Local Sourcing. Es empfiehlt sich eine weitere Untersuchung hinsichtlich der Kosten, die durch Resilienz-Instrumente sowie durch fehlende Resilienz entstehen können, durchzuführen.
Der Automobilhandel befindet sich in einer momentanen Phase des Umbruchs. Der Trend zum Kauferlebnis online, verstärkt durch die Pandemie, und die durch die Dieselproblematik entfachte Diskussion zum Emissionsausstoß und Fahrverboten für Verbrennungsmotoren im Fahrzeugbereich, treibt den Wandel zu elektrischen Antriebstechnologien an. Durch diese Umstellung ergeben sich neue Möglichkeiten für das Fahrzeug im Bereich der Digitalisierung. Die branchenübergreifende Digitalisierung findet sich auch in den neuen Vertriebssystemen der Automobilhersteller wieder. Hierbei ist zudem eine Veränderung der Absatzkanäle zu beobachten. Der Kunde weicht beim Autokauf davon ab, exklusiv zu dem Händler seines Vertrauens zu gehen und sich hier durch den gesamten Verkaufsprozess hinweg betreuen zu lassen. Vielmehr verschieben sich einzelne Teilbereiche in das Internet. So müssen etablierte Prozesse neugestaltet werden, um weiterhin im Markt und im Wettbewerb bestehen zu können.
Das Ziel dieser Arbeit ist die Beantwortung zweier Forschungsfragen. Zum einen soll der Status-Quo der Digitalisierung von Vertriebsinstrumenten im Autohaus untersucht werden. Konkret geht es dabei um die Frage, in welcher Art und Weise die Digitalisierung den Vertrieb beeinflusst und welche Effekte sich daraus ergeben. Zum anderen stellt sich die Frage wie die Digitalisierung in Form des Internets, als Absatzkanal für die Automobilindustrie geeignet ist, und ob derart komplexe Produkte wie ein Neufahrzeug über diesen Kanal abgesetzt werden können. Weiterhin soll betrachtet werden, welche Veränderungen in den Vertriebsstrukturen das beim stationären Autohändler verursacht.
Die Arbeit befasst sich ausschließlich mit der Digitalisierung bei Vertragshändlern mit Volumenzielen im Neuwagen-Bereich. Damit sind die Luxus- oder Premiummarken ausgeschlossen. Außerdem wird im Autohaus in zwei Kundenkategorien unterschieden. Es gibt die Großabnehmer und die Einzelkunden. Die Großabnehmer sind Unternehmen, die durch eine vordefinierte Anzahl an Mindestabnahmen einen besonderen Status und Konditionen genießen. Die Einzelkunden sind private Abnehmer oder gewerbliche Kunden, die die Mindestabnahme nicht erreichen. Die Arbeit beschränkt sich ausschließlich auf die Analyse und Auswirkungen der Veränderungen im Segment der Einzelkunden.
Abstract: This paper is about detecting the difference between fully-random and semi-random shuffleing data sets, with the use of unsupervised learning algorithms. Because of the limits of the k-means algorithm alone, a recurrent autoencoder is used for feature extraction to improve the results of k-means. In the next step the autoencoder alone is used for clustering.
Introduction: In the last years, machine learning has been used more and more in different areas and it is also appropriate for for pattern recognition in data. Random data is characterized through the missing of defined patterns. Permutations without repetitions have the highest amount of entropy for a sequence of its length, which is similar to random data according to Andrei Kolmogorov, who states that random data have the highest amount of information and can’t be compressed. Therefore, this paper analyses the difference between random permutations and good shuffled permutations, which have some remaining patterns left. This is done via a recurrent autoencoder.
Universities, as innovation drivers in science and technology worldwide, should attempt to become carbon-neutral institutions and should lead this transformation. Many universities have picked up the challenge and quantified their carbon footprints; however, up-to-date quantification is limited to use-phase emissions. So far, data on embodied impacts of university campus infrastructure are missing, which prevents us from evaluating their life cycle costs. In this paper, we quantify the embodied impacts of two university campuses of very different sizes and climate zones: the Umwelt-Campus Birkenfeld (UCB), Germany, and the Nanyang Technological University (NTU), Singapore. We also quantify the effects of switching to full renewable energy supply on the carbon footprint of a university campus based on the example of UCB. The embodied impacts amount to 13.7 (UCB) and 26.2 (NTU) kg CO2e/m2•y, respectively, equivalent to 59.2% (UCB), and 29.8% (NTU), respectively, of the building lifecycle impacts. As a consequence, embodied impacts can be dominating; thus, they should be quantified and reported. When adding additional use-phase impacts caused by the universities on top of the building lifecycle impacts (e.g., mobility impacts), both institutions happen to exhibit very similar emissions with 124.5–126.3 kg CO2e/m2•y despite their different sizes, structures, and locations. Embodied impacts comprise 11.0–20.8% of the total impacts at the two universities. In conclusion, efficient reduction in university carbon footprints requires a holistic approach, considering all impacts caused on and by a campus including upstream effects.
The data presented here contain information on cheating behavior from experiments and general self-reported attitudes related to honesty-related social norms and trust, together with individual-level demographic variables. Our sample included 493 university students in five countries, namely, Germany, Vietnam, Taiwan, China, and Japan. The experiment was monetarily incentivized based on the performance on a matrix task. The participants also answered a survey questionnaire. The dataset is valuable for academic researchers in sociology, psychology, and economics who are interested in honesty, norms, and cultural differences.
Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSCc). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.
Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.