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There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap “Biology in Space and Analogue Environments” focusing on “How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?” The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.
This contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10−4 is found at the plate’s surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10−4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm−1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic.
In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier.
Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
(2024)
This paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
Smart Cities
In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly.
Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors.
Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K.
Modellierung des Antriebsstrangs einer Textilmaschine zum Zweck der modellbasierten Steuerung
(2024)
Zur Produktion von gewirkten Textilien werden mehrere Nadeln auf eine Legebarre gesetzt, die durch Servoantriebe positioniert werden. Aufgrund der erzwungenen Bewegung kann der Antriebsstrang bei hohen Drehzahlen zur Schwingung angeregt werden. Die richtige Wahl der Steuerkurven ist daher eine sehr wichtige und anspruchsvolle Aufgabe, die durch ein Antriebsstrangmodell unterstützt werden soll. Dafür wird am Beispiel eines Teststands ein Modell des Antriebsstranges in MATLAB/Simulink® entwickelt. Für das Antriebsstrangmodell müssen eine geeignete Modellordnung und Modellparameter gewählt werden. Die unbekannten Modellparameter werden durch eine Parameteridentifikation ermittelt. Mit einer Validierung wird ein geeignetes Anregungsspektrum für die Bestimmung der Modellordnung und die Parameteridentifikation ermittelt.
Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.
The Eurosystem and the Deutsche Bundesbank will incur substantial losses in 2023 that are likely to persist for several years. Due to the massive purchases of securities in the last 10 years, especially of government bonds, the banks' excess reserves have risen sharply. The resulting high interest payments to the banks since the turnaround in monetary poli-cy, with little income for the large-scale securities holdings, led to massive criticism. The banks were said to be making "unfair" profits as a result, while the fiscal authorities had to forego the previously customary transfers of central bank profits. Populist demands to limit bank profits by, for example, drastically increasing the minimum reserve ratios in the Eurosystem to reduce excess reserves are creating new severe problems and are neither justified nor helpful. Ultimately, the EU member states have benefited for a very long time from historically low interest rates because of the Eurosystem's extraordinary loose monetary policy and must now bear the flip side consequences of the massive expansion of central bank balance sheets during the necessary period of monetary policy normalisa-tion.
Due to the massive purchases of securities in the last 15 years central banks incur substantial losses likely to persist for several years. On the other hand, the banking sector gains large profits from interest payments on their excess reserves holdings. Central banks and fiscal authorities must now bear the flip side consequences of their bond purchase programs. Populist demands to limit bank profits by drastically increasing minimum reserve ratios in the Eurosystem are creating new severe problems. Instead, a consistent and faster
normalisation of central bank balance sheets would be desirable. Central banks should also no longer be central players in government bond markets to restore the lost boundaries between fiscal and monetary policy.
Alte Weisheiten wie „Schuster bleib bei deinem Leisten!“ oder auch „Was das Hänschen nicht lernt, das lernt der Hans nimmermehr“ haben in der heutigen, sich wandelnden Arbeitswelt ihren Sinn verloren. Das bedeutet, dass sich die komplette Belegschaft lebenslang weiterbilden muss, idealerweise mit Unterstützung des Arbeitgebers.
In der Veröffentlichung "Chief Qualification Officers (CQOs) und Weiterbildungsmentoren - Thesen und Argumente" wird unter anderem das paradoxe Verhältnis von Arbeitslosigkeit und Fachkräftemangel erläutert, das Marktversagen im Weiterbildungsmarkt beschrieben und die Forderung nach einem Weiterbildungsbeauftragten (CQO) begründet.
Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, “Biology in Space and Analogue Environments”, focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: “How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?” The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed.
Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.
This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole
training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters
of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this
way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift’s dynamic is approximated
within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori
knowledge within the control design. Therefore, the
Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared
to an RL control concept, which is not considering the disturbance variable and to a classical
two-degrees-of-freedom (2DoF) control approach.
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
(2023)
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
A novel 3D-printed glucose sensor is presented for cell culture application. Glucose sensing was performed using a fluorescence resonance energy transfer (FRET)-based assay principle based on ConA and dextran. Both molecules are encapsulated in alginate microspheres and embedded in the UV-curable, stable hydrogel polyvinyl alcohol (PVA). The rheology of the formulation was adapted to obtain good properties for an extrusion-based printing process. The printed sensor structures were tested for their ability to detect glucose in vitro. A proportional increase in fluorescence intensity was observed in a concentration range of 0 - 2 g/L glucose. Tests with HEK cell cultures also showed good cell compatibility and excellent adhesion properties on plasma-treated Petri dishes. The printed sensors were able to detect the glucose decay associated with the metabolic activities of the fast-growing HEK cells in the cell culture medium over ten days. The proof-of-principle study shows that metabolic processes in cell cultures can be monitored with the new printed sensor using a standard fluorescence wide-field microscope.
In this article, we contribute to the longstanding debate among economists regarding the question of “nature or nurture” with respect to economics students’ attitudes toward various allocation mechanisms for a scarce resource. While previous research starts the debate by beginning with first-year economics students, we aim to evaluate pre-firstyear individuals, i.e., school pupils. Drawing on the seminal works of Haucap, J., & Just, T. (2010). Not guilty? Another look at the nature and nurture of economics students. European Journal of Law and Economics, 29(2), 239–254 and Frey, B. S., Pommerehne,W.W., & Gygi, B. (1993). Economics indoctrination or selection? Some empirical results. The Journal of Economic Education, 24(3), 271–281, we investigate a sample of pupils ranging from the 5th to the 13th grades to determine whether pupils are “born economists” (nature), develop economic thinking (nurture), or both. We find that young individuals start to think differently in early grades and that their thinking and attitudes are shaped differently throughout their school careers, thereby providing support for the effects of both nature and nurture. Our findings show that school time impacts fairness judgments, particularly regarding price mechanisms. Regarding learning or indoctrination, we find that economics-inclined pupils are positively affected by lessons in economics in school, while pupils who are economics-averse draw completely diametric conclusions from economics lessons, thereby exhibiting increased disapproval of price allocation over the course of these classes and increased approval of the first come, first served and governmental action mechanisms.Moreover, we find strong effects of gender and migration background in this context. This study is the first to elucidate the development of economic thinking in 5th–13th grade pupils. Our results are important for economists, educators,
and researchers because they can serve as a starting point for subsequent investigations in this under-researched field.
In 1895, Professor Wilhelm Conrad Röntgen detected a new type of radiation that was able to penetrate solid materials, which he called X-rays. In 1901 Röntgen received the first Nobel Prize in physics in honour of this ground breaking
discovery. The Deutsches Röntgen-Museum (DRM) in Remscheid (Germany) is the institution that uniquely and
comprehensively explores and documents the life and work of W. C. Röntgen and the impact of his discovery. The DRM’s
location in Remscheid is not coincidental, with Röntgen’s birthplace only a short walk away from the exhibits. Every visit to the museum amounts to a unique expedition through the worlds of medicine, science and technology. The museum’s emphasis on the diversity of Röntgen’s invention by a multilingual, multi-medial approach enables all visitors to make their own personal discoveries. The Deutsches Röntgen-Museum in Remscheid is a must-see for X-ray scientists from anywhere in the world. This contribution provides an insight into the history of X-rays and offers a guided tour of the Deutsches Röntgen-Museum and its exhibits.
Starting in 1951, the 50th anniversary of the award of the first Nobel Prize for physics to Wilhelm Conrad Röntgen, the Lord Mayor of the German City of Remscheid has given out Röntgen Medals. The Röntgen Medal annually honours
scientists who "in the broadest sense have made a special contribution to the progress and dissemination of X-ray
discoveries in the theoretical and applied sciences". The Röntgen Medal has become highly recognized in the scientific world. To date, more than one hundred excellent scientists have received the honour. Through their chronology, this
contribution presents a "Who’s Who" of X-ray science and provides selected insights into their scientific work; with the fields of X-ray optics and X-ray astronomy receiving special focus.
Conventional mining might not be sufficient to support the growth of humankind which is heavily dependent upon rare materials in technical applications. Asteroid mining might be an answer, with Near-Earth objects (NEOs) being the first targets. However, the first step in the asteroid mining cascade is to probe reachable
asteroids. Moreover, to identify the best candidates for further activities hundreds to thousands of asteroids must be screened. The fast progressing development of CubeSats might allow the space community to do the initial in-situ screening in a minimalistical and economical manner. Additionally, formation flying might enable the miniaturization of optical payloads for asteroid composition analysis in CubeSats. The recent developments in formation flying are summarised in this study and the possibility of utilizing formation flying CubeSats for
asteroid surveillance explored.
In diesem Jahr jährte sich der Todestag von Wilhelm Conrad Röntgen, gestorben am 10. Februar 1923, zum hundertsten Mal. Dieser Beitrag ist eine Hommage auf diesen bedeutenden deutschen Wissenschaftler. 1895 entdeckte er eine neue Art von Strahlung, welche in der Lage ist, feste Materialien zu durchdringen. 1901 erhielt Professor Röntgen für diese bahnbrechende Entdeckung den ersten Nobelpreis für Physik. Das Deutsche Röntgen-Museum (DRM) erforscht und dokumentiert das Leben und Werk von W. C. Röntgen und die Auswirkungen seiner Entdeckung auf eine einzigartige und umfassende Weise. Der Standort des DRM in Remscheid ist kein Zufall, denn Röntgens Geburtshaus liegt nur wenige Gehminuten von den Exponaten entfernt. Die Betonung der Vielfalt von Röntgens Erfindung durch das Museum mit einer multimedialen Herangehensweise ermöglicht es den Besuchern, hier ihre ganz persönlichen Entdeckungen zu machen. Jeder Museumsbesuch wird so zu einer einzigartigen Entdeckungsreise durch die Welt der Medizin, Wissenschaft und Technik. Dieser Beitrag würdigt das Lebenswerk von Professor Wilhelm Conrad Röntgen und gibt einen Einblick in die Geschichte der Röntgenstrahlen und ihre Anwendungsgebiete.
„Freude habe ich nur, solange ich es mit dem Experimentieren zu tun habe“, schrieb Wilhelm Conrad Röntgen am 9.6.1920 an Margret Boveri. Das Deutsche Röntgen-Museum und das Remscheider Röntgengymnasium geben getreu diesem Motto Kindern und Jugendlichen die Möglichkeit, selbst aktiv zu werden, zu experimentieren und so auf den Spuren dieses bedeutenden Forschers zu wandeln. Hierzu wurde am 8. November 2011, einem Jahrestag der Entdeckung der Röntgenstrahlen, das Museumslabor RöLab gegründet. Dort werden Themenbrücken in das tägliche Leben geschlagen und so Lernprozesse im MINT-Bereich für die jungen Forschenden anregend, attraktiv und nachhaltig erlebbar. Das RöLab versteht sich als ein überregionales, außerschulisches Bildungsangebot, das Begeisterung für Natur- und Ingenieurwissenschaften, Medizin und Medizintechnik an unterschiedliche Zielgruppen vermittelt und damit einen Beitrag zur Allgemeinbildung und MINT-Förderung leistet. Das didaktische Konzept nimmt den „Hands-on-Science“ Gedanken stringent auf, weckt den Forschergeist und ermöglicht es den Lernenden, Unsichtbares zu erkunden. Dieser Beitrag stellt das Museumslabor RöLab des Deutschen Röntgen-Museums vor.
Due to the recent progress in satellite control engineering, tandem flight arrangements of mini-satellites are currently in discussion and
under consideration for future space missions. CubeSat technologies offer an interesting possibility for space-born telescope payloads
based on this approach. In such a scenario, two small satellites will fly at a constant distance of a few meters up to 100 meters. Thereby,
the front satellite will carry the telescope optics and the second satellite the detector system. Related formation flight requirements were
addressed in the NetSat-Mission, composed of four 3U-CubeSats, launched 2020. Space technology challenges concern in particular orbit
control by an appropriate propulsion system as well as precision attitude pointing towards the joint target, and could be transferred from the
NetSat-mission to the proposed Tandem Mission. This will allow space-based telescopes with a long focal length even placed on board of
small or miniature CubeSat-type satellites. Cost effective Kirkpatrick-Baez type X-ray telescopes represent an important application here as
they have longer focal lengths compared to previous astronomical observatories using Wolter I type X-ray mirrors.
Die voranschreitende Digitalisierung und insbesondere der Einsatz von Künstlicher Intelligenz (KI) in der
Bildung eröffnen neue Möglichkeiten des Lernens. Bildung kann stärker individuell sowie zeitlich und
räumlich entgrenzt stattfinden. ChatGPT verdeutlicht, wie dynamisch die Entwicklungen im Bereich KI
sind. Das in Zunahme begriffene Interesse am Thema KI allgemein sowie KI in der Bildung kommt dem
erforderlichen Austausch zwischen zentralen Akteur:innen zugute. Dieses Diskussionspapier präsentiert
drei Zukunftsszenarien für den Einsatz von KI in der institutionellen Bildung: Hochschule, Weiterbildung
und Schule. Im Fokus stehen die übergeordneten Fragestellungen nach Voraussetzungen und
Gelingensbedingungen einer erfolgreichen Anwendung sowie nach gemeinsamen Handlungsfeldern
für zentrale Stakeholder. Fünf Handlungsfelder für den Einsatz von KI in institutioneller Bildung sind auf
Grundlage der skizzierten Zukunftsszenarien hervorzuheben: (1) Interdisziplinäre Zusammenarbeit, (2)
Qualifizierungsangebote und Kompetenzentwicklung, (3) Digitale Infrastruktur und Personal, (4) Ethik
und Datensouveränität sowie (5) Interoperabilität von Daten in Bildungskontexten. Ziel des
vorliegenden Diskussionspapiers ist es, den kooperativen Austausch zwischen relevanten Stakeholdern
anzuregen.
Das RöLab versteht sich als ein überregionales, außerschulisches Bildungsangebot, das Begeisterung für Natur- und Ingenieurwissenschaften, Medizin und Medizintechnik an unterschiedliche Zielgruppen vermittelt und damit einen Beitrag zur Allgemeinbildung und MINT-Förderung leistet. Das didaktische Konzept nimmt den „Hands-on-Science“ Gedanken stringent auf, weckt den Forschergeist und ermöglicht es den Lernenden, Unsichtbares zu erkunden.
2023 jährte sich der Todestag von Wilhelm Conrad Röntgen zum einhundertsten Mal. Im Jahr 1895 entdeckte er eine neue Art von Strahlung, die feste Materialien durchdringen kann. 1901 erhielt er für diese Entdeckung den ersten Nobelpreis für Physik. Das Deutsche Röntgen-Museum erforscht und dokumentiert das Leben und Werk von W. C. Röntgen und die Auswirkungen seiner Entdeckung.
Progress in satellite control engineering enables tandem flight arrangements of mini-satellites in future space missions. Cost effective CubeSat technologies offer an interesting possibility for space-born telescope payloads. Two small satellites will carry the telescope optics and the detector system, respectively. Related formation flight requirements could be transferred from the NetSat-Mission.
Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
Manipulation von Abgaswerten
(2023)
Dieses kleine Buch stellt Hintergründe zum Abgasskandal dar, z. B. welche Schadstoffe kritisch sind, wie Motorsteuergeräte, Abgasrückführung und Abgasnachbehandlung funktionieren, wie Manipulationen auf dem Prüfstand möglich sind, mit welchen Fahrzyklen getestet wird und in welchem rechtlichen und politischen Umfeld dies lange Zeit unbemerkt mit unterschiedlichen Konsequenzen in der EU und den USA geschehen konnte. Es werden Maßnahmen vorgestellt, mit denen Fahrzeuge auch real gesetzliche Grenzwerte einhalten können und wie Manipulationen zukünftig rechtlich und politisch verhindert werden können. Dem Leser werden durch Literaturhinweise zusätzliche Details erschlossen.
Erste Schritte zur Implementierung eines Nachhaltigkeitsmanagements im Unternehmen
Dieser kompakte Band geht auf zentrale Prinzipien und Konzepte ein, die Unternehmen helfen, ein Nachhaltigkeitsmanagement aufzubauen und weiterzuentwickeln. Darüber hinaus bietet er einen Überblick über die verschiedenen Ansätze und Instrumente des Nachhaltigkeitsmanagements, damit Unternehmen ihre Aktivitäten analysieren, bewerten und verbessern können, um ökologische und soziale Auswirkungen zu reduzieren und langfristige Wertschöpfung zu ermöglichen.
Diese grundlegende Einführung richtet sich an die Führungskräfte in den Unternehmen, die sich mit Fragen des Umwelt- und Nachhaltigkeitsmanagements beschäftigen. Zunächst werden die relevanten normativen und regulativen Anforderungen an das unternehmerische Nachhaltigkeitsmanagement vorgestellt, bevor konkret erste Schritte zur Implementierung eines Nachhaltigkeitsmanagements entwickelt werden. Die beiden letzten Kapitel stellen ein geeignetes Steuerungssystem sowie die Grundlagen der Nachhaltigkeitskommunikation eines Unternehmens vor.
In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial Intelligence (AI). The lateral control is realized using a Model Predictive Controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC.
Das Buch vermittelt die Grundlagen, um die Besonderheiten der Elektronik und Software im Kfz nicht nur zu kennen, sondern auch zu verstehen. Zusätzlich wird an Beispielen die Komplexität realer Systeme im Fahrzeug vorgeführt und gezeigt, welche Anwendungen durch die Elektronik erst möglich werden. Das Spannungsfeld zwischen Sicherheit, Zuverlässigkeit und Komplexität prägt in Verbindung mit branchenüblichen Abläufen das Vorgehen bei der Entwicklung, das ein in diesem Bereich tätiger Ingenieur verstehen muss.
The present white paper concerns the indications and recommendations of the SciSpacE Science Community to make progress in filling the gaps of knowledge that prevent us from answering the question: “How Do Gravity Alterations Affect Animal and Human Systems at a Cellular/Tissue Level?” This is one of the five major scientific issues of the ESA roadmap “Biology in Space and Analogue Environments”. Despite the many studies conducted so far on spaceflight adaptation mechanisms and related pathophysiological alterations observed in astronauts, we are not yet able to elaborate a synthetic integrated model of the many changes occurring at different system and functional levels. Consequently, it is difficult to develop credible models for predicting long-term consequences of human adaptation to the space environment, as well as to implement medical support plans for long-term missions and a strategy for preventing the possible health risks due to prolonged exposure to spaceflight beyond the low Earth orbit (LEO). The research activities suggested by the scientific community have the aim to overcome these problems by striving to connect biological and physiological aspects in a more holistic view of space adaptation effects.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. The discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.
Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style
(2023)
The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing.
Reflective coatings for astronomical X-ray optics were developed at the “Aschaffenburg Competence Center for Astronomical and Space Instrumentation” (ACCASI) since several years. As part of a Bavarian-Czech cooperation between the Technical University of Aschaffenburg and the Czech Technical University of Prague, now two mechanically identical telescopes were built. One telescope optic was equipped with conventional gold-coated mirrors, manufactured by the Czech project partners. The 34 X-ray mirrors of the second telescope use an innovative coating system made of chromium and iridium, which was applied at the Aschaffenburg coating laboratory. Both telescopes are designed according to the bionic principle of a reflecting lobster eye. The optics works with two consecutive reflections on mutually perpendicular mirror surfaces. This enables a large field of view with many square degrees in diameter, which, however, comes at the price of a reduced angular resolution. An extensive X-ray characterization of these telescopes was carried out at the PANTER test facility of MPE, which simulates parallel starlight incident on the telescopes. The telescopes have an angular resolution of about 4 arc minutes in X-rays and a focal length of about 2 meters. Furthermore, the used X-ray mirrors reflect and focus visible light as well and this functionality in the optical regime was checked in laboratory tests. Now another test campaign was done to examine the telescope resolution for real objects of the visible sky and the imaging properties for star constellations. Such functional tests by observing astronomical objects of the visible sky may simplify and accelerate the development of X-ray telescopes for satellite applications.
This contribution demonstrates and discusses the preparation of finely dispersed copper(II) oxide nanosuspensions as precursors for reductive laser sintering (RLS). Since the presence of agglomerates interferes with the various RLS sub-processes, fine dispersion is required, and oversized particles must be identified by a measurement methodology. Aside from the established method of scanning electron microscopy for imaging individual dried particles, this work applies the holistic and statistically more significant laser diffraction in combination with dynamic image analysis in wet dispersion. In addition to direct ultrasonic homogenization, high-energy ball milling is introduced for RLS, to produce stable nanosuspensions with a high fine fraction, and, above all, the absence of oversize particles. Whereas ultrasonic dispersion stagnates at particle sizes between 500 nm and 20 μm, even after 8 h, milled suspension contains a high proportion of finest particles with diameters below 100 nm, no agglomerates larger than 1 μm and a trimodal particle size distribution with the median at 50 nm already, after 100 min of milling. The precursor layers produced by doctor blade coating are examined for their quality by laser scanning microscopy. The surface roughness of such a dry film can be reduced from 1.26 μm to 88 nm by milling. Finally, the novel precursor is used for femtosecond RLS, to produce homogeneous, high-quality copper layers with a sheet resistance of 0.28 Ω/sq and a copper mass concentration of 94.2%.
Evaluation of an ultrashort pulsed laser robot system for flexible and large-area micromachining
(2023)
We report for the first time on the realization, characterization and application of an ultrashort pulsed laser robot system for flexible and large-area 2D and 3D laser micromachining with 6 articulated axes. To characterize the dynamic positioning of the laser beam during and after axes movement, CMOS image sensors were integrated into the beam path. A method introduced for the alignment of the optical axes allows a reduction of the deviations in laser beam positioning to less than 141.8 ± 92.9 μm within a 110° rotation range of axis 4. In addition, a high laser beam positioning repeatability of less than 102.2 μm is demonstrated over a total period of 14 h for a movement of axis 5 within a range of 0° to 90°. Initial laser cutting, laser structuring and laser marking applications on automotive dashboards and glass substrates are presented for flexible and large area 2D and 3D manufacturing. By applying a special laser cutting strategy for processing AF 32 eco thin glass, high cutting quality is achieved with a taper of up to 96.3% without the generation of cracks, demonstrating the innovative potential of the high-precision laser robot system. Nonetheless, different identified inherent influences of each axis 1–5 during robot axis movement demand for an innovative beam stabilization concept to achieve high precision in laser beam positioning.
We report on an experimental study of laser microvia percussion drilling of Ajinomoto build-up film (ABF) material using an ultrashort pulsed laser in the ultraviolet region. In order to optimize the drilling quality in terms of the fabricated taper and microvia diameter, the
MHz burst mode is used in combination with both a Gaussian and a top-hat laser intensity distribution. To evaluate the influence of the top-hat beam shape on defocusing, microvias were drilled at different z-positions in the range of 200 μm evenly distributed around the focal plane. The drilling quality was evaluated by laser scanning microscopy and metallography. When using a top-hat beam shape, a reduced taper of down to 26.8 % on average is achieved compared to a Gaussian beam. For the application of 3 intra-burst pulses, we find an improvement of the taper by 11.6% compared to the single pulse mode due to the increasing heat accumulation. Using a top-hat beam shape in combination with the MHz burst mode, an 18.2 % reduction in laser drilling time for a microvia with a similar taper is remarkable, demonstrating the high potential for scaling throughput in electronics manufacturing.
We demonstrate and discuss the integration of Bragg gratings in aerosol-jetted polymer optical waveguides, produced in the optical assembly and connection technology for component-integrated bus systems (OPTAVER) process. By using a femtosecond laser and adaptive beam shaping, an elliptical focal voxel generates different types of single pulse modification by nonlinear absorption in the waveguide material, which are arranged periodically to form Bragg gratings. Integration of a single grating structure or, alternatively, an array of Bragg grating structures in the multimode waveguide yields a pronounced reflection signal with typical multimodal properties, i.e., a number of reflection peaks with non-Gaussian shapes. However, the main wavelength of reflection, located around 1555 nm, is evaluable by means of an appropriate smoothing algorithm. When loaded by mechanical bending, a pronounced Bragg wavelength shift of this reflected peak up to 160 pm is detected. This demonstrates that the additively manufactured waveguides can be used not only for signal transmission but also as a sensor.
We report on ultraviolet ultrashort pulsed laser processing of silicon carbide. Laser ablated cavities are evaluated with respect to their ablation rates, surface roughness and overall quality by varying the laser pulse fluence. Using this type of laser, high edge quality and surface roughness down to 107 nm can be achieved with an ablation efficiency of up to 0.228 mm 3 W −1 min −1. Functional surfaces are produced by generating laser induced periodic surface structures. The comparison with infrared wavelength underlines the advantages of the ultraviolet wavelength for laser micro machining this material. Significant differences with respect to the measured ablation depths and roughness as well as generated micro-and nanostructures appear. While infrared ablation is dominated by a chipping mechanism above a critical fluence of 9.93 Jcm −2 , higher ablation rates are observed with strong quality losses at the same time. In comparison to the infrared emission wavelength, in general , a significantly higher processing quality is achieved with the ultraviolet emission wavelength. In addition, the influence of spot size and repetition rate in UV processing is investigated. By increasing these parameters, a process acceleration without quality losses is enabled. The generation of a sophisticated microstructure exemplifies the advantages of processing silicon carbide with the UV laser reported here.
We report on the acceleration of femtosecond reductive laser sintering of copper (II) oxide on the surface of transparent cyclic olefin copolymers (COC) by varying the focus size to generate metallic copper (Cu) layers. By using different focusing conditions, different process speeds are achieved, and the structural accuracies are controlled. The formation of Cu lines is observed for different focus sizes at various scan speeds, which is attributed to laser exposure duration and chemical reaction time. The formed lines are categorized and measured by optical microscopy and provide the basis for two-dimensional Cu surfaces. By hatching, high quality conductive films are produced with an increased focus diameter of up to 400 µm at a scan speed of 400 mm/s and a hatch distance of 250 µm resulting in a multiple increased process speed by a factor of thirteen compared to previous research. Using 4-tip measurement , a low sheet resistance of 0.165 Ω/sq was validated on these Cu films, which have an atomic Cu content of 89.5%.
While sapphire is one of the most durable materials, its properties entail that high-precision machining, especially in the sub-millimeter regime, is still challenging. This contribution demonstrates and discusses novel femtosecond laser-based micromachining approaches for the fabrication of rotational-symmetric sapphire workpieces, specifically the generation of optical fibers by means of laser lathe of sapphire rods and the practical realization of windmill fibers. In addition, volume refractive index modification in planar sapphire substrates is presented to induce photonic crystal waveguides. The micromachined structures are comprehensively examined with respect to geometric fidelity, surface roughness, refractive index modification, and potential optical waveguiding properties. All micromachining approaches are done by means of frequency-doubled or frequency-tripled femtosecond laser radiation. Different laser optical setups including laser scanning head, spatial beam profilers including a spatial light modulator and axial rotatory movement of the specimen are employed for micro structuring and in-depth refractive index modifications. In particular for laser lathe, a sophisticated scanning pattern, in combination with an incremental axial rotatory movement of the specimen, allows for the precise diameter reduction of sapphire rods with 250 µm diameter to fibers with outer diameters of 25 µm. By supporting the workpiece with a V-groove fixture, multi-mode fibers with lengths up to 20 cm can be processed with an average surface roughness of 250 nm. Additionally, an adapted ablation scanning sequence enables the first practical demonstration of sapphire windmill fibers. Furthermore, using a spatial light modulator allows for the adaption of the laser propagation properties as to enable volume refractive index modifications with free-form arrangement. Hexagonal patterns of refractive index modifications surrounding a pristine waveguide core are fabricated and single-mode waveguiding at 1550 nm is verified. Finally, the possibility of integrating Bragg gratings into this photonic waveguide type is demonstrated
While Bragg grating-based optical devices have shown promising performances for pressure sensing applications, their sensitivity, especially in the low-pressure regime, is unsatisfying and needs to be optimized by elaborate designs, such as cantilevers or other extrinsic mechanical transducers. This contribution demonstrates and discusses a novel concept for optical pressure sensors based on polymer planar Bragg gratings. Waveguide and Bragg grating are fabricated underneath the surface of a temperature-stable and humidity-insensitive cyclic olefin copolymer substrate by means of a femtosecond laser. Based on the employed direct-writing procedure, in combination with adaptive, in-situ beam shaping with a spatial light modulator, writing depth, i.e., location of the photonic structures within the substrate, as well as Bragg grating periodicity and positioning can be deliberately chosen. Afterwards, the polymer substrate is post-processed with a high-precision micro mill, so a diaphragm comprising the integrated photonic structures is generated. The resulting diaphragm exhibits a thickness of 300 µm and a diameter of 10 mm. Finally, the optical sensor is packaged and sealed to form an air-filled gas pocket underneath the diaphragm. Deformations of the diaphragm by external pressure changes translate to strain variations along the waveguide axis and thus perturb the Bragg grating period. This leads to changes in the grating’s wavelength of main reflection, which can be evaluated in order to quantify the relative external pressure. With this straightforward optical sensor concept, pressure sensitivities up to 39 pm kPa-1, within relative pressures ranges from 78 kPa to 372 kPa, are achieved.
Livable cities measure quality-of-life factors such as transportation, convenience of daily life, education, and a safe and stable built and natural environment. Livability of a city includes also some social and psychological factors, like emotion and perception. How do we realize the advantages of new technology under mixed traffic conditions, while observing all daily requirements on safety, convenience and high education needs?
We report on a comprehensive study of laser percussion microvia drilling of Ajinomoto build-up film (ABF) material using an ultrashort pulsed laser in MHz burst mode. After laser processing, microvia drilling quality is being evaluated by the fabricated diameter and taper using laser scanning microscopy and metallography. The influences of the incubation effect, heat accumulation and shielding effects as a result of pulse to pulse interactions are being discussed on the ablation threshold, penetration depth and laser microvia drilling quality. We find that an increasing heat accumulation in MHz burst mode processing is responsible for the void formation and delamination of the insulating ABF layer. Therefore, the parameter clearance is introduced to evaluate these effects on the microvia sidewalls. For a comparable clearance, applying 2 intra-burst pulses achieves an average reduced taper of down to 19.5% compared to single pulse mode. At the same time, a reduced laser drilling time of 16.7% per microvia highlights the enormous potential of the MHz burst mode for laser drilling of ABF material in printed circuit board fabrication.
内燃机测量技术和试验台架
(2023)
Das Eurosystem und auch die Deutsche Bundesbank werden 2023 Verluste erzielen, die einige Jahre anhalten dürften. Aufgrund der massiven Käufe von Wertpapieren in den letzten 10 Jahren, vor allem Staatsanleihen, sind die Überschussreserven der Banken stark angestiegen. Die dadurch bedingten hohen Zinszahlungen an die Banken seit der geldpolitischen Kehrtwende, bei geringen Einnahmen für die hohen Wertpapierbestände, führten zu massiver Kritik. Die Banken würden dadurch „ungerechte“ Gewinne erzielen, während die Fiskalbehörden auf die früher üblichen Überweisungen der Zentralbankgewinne verzichten müssen. Populistische Forderungen nach einer Begrenzung der Bankengewinne durch z.B. eine pauschale, drastische Erhöhung der Mindestreservesätze im Eurosystem zur Reduzierung der Überschussreserven bergen neue Probleme und sind nicht hilfreich. Letztlich haben die Staaten sehr lange von historisch niedrigen Zinsen infolge der expansiven Geldpolitik des Eurosystems profitiert und müssen nun die Folgen der massiven Aufblähung der Zentralbankbilanzen während des Zeitraums der Normalisierung der Geldpolitik tragen.
Der Vorschlag einer drastischen Erhöhung des Mindestreservesatzes durch die EZB erhitzt die Gemüter. Nach teils heftiger Kritik hochrangiger Bankenvertreter in Deutschland werden die „üblichen“ Beschuldigungen vorgebracht und von Medien z.T. unreflektiert wiedergegeben bzw. kommentiert. Die Banken erhielten seit Herbst 2022 – so das gängige Muster - risikolose und ungerechte Subventionen und wüssten nichts Besseres mit ihren Einlagen bei der Bundesbank anzufangen.
Diese Kritik greift zu kurz und ist teils sachlich falsch.
An integrative definition of digital, innovation, and sustainable: A bridge between disciplines
(2023)
It needs to be clarified how digital, innovation, and sustainable concepts are interdependent and what dimensions they encompass from an organizational perspective. This article aims to fill these gaps. 12,249 articles are analyzed, of which 776 are minimum C-ranked according to VHB-Jourqual, and 457 pass the quality filters. Only 48 publications deal with all three concepts, and 24 offer definitions. This results from an eight-step systematic literaturereview. Three dimensions for 'digital' (data, technologies, and networks), three dimensions for ‘sustainable’(environmental, social, and economic), and four dimensions for ‘innovation’(product, service, business model, and process) are elaborated in a framework. Based on this, an integrated definition is derivedusing a content analysis.The DIS-definition can be used for strategic decision-making processes and bridges the disciplines of technology, innovation, and sustainability management. Finally, a canvas that clarifies the concepts and their connections is proposed for interdisciplinary management discussions.
Evaluating the impact of deviating technical standards on business processes, trade and innovation
(2023)
Surrounding the increasingly intense discussions about the emergence of new global standardization regimes in context of China’s rise as a dominant standardization power, there has been much talk about countries purposefully using deviating national standards to impose trade barriers. The discussion of whether and to what degree technical standards deviate from international standards and how this affects business, trade, innovation and the standard system is of global relevance. As research about the impact of deviating technical standards is still strongly underrepresented in the academic community, this research analyses the different “degrees” of deviation and the respective impact of minor or negligible deviation and strong deviation on businesses trading in a global context. By using a mixed research method based on literature review, analysis of standard documents and semi-structured interviews, this study discusses peculiarities and challenges associated with deviating technical standards. This is of relevance with regards to international trade and especially trade with countries that became increasingly important players in the international standardization regime. Our research will therefore add further insights to a better understanding of the close linkage between economic growth and standardization. This paper further highlights how deviating technical standards impact companies around the globe and how these companies could use a newly developed risk indicator to not only engage in the standard game but also to better assess consequences.
Die vorliegende Studie untersucht Entwicklungen und Trends im Nachhaltigkeitscontrolling, insbesondere hinsichtlich der strategischen Bedeutung der fünf Stufen der Nachhaltigkeit, des Einflusses von Stakeholdergruppen, Zielsetzungen und Instrumenten anhand von drei Studien des Fachkreis Green Controlling for Responsible Business und gibt Implikationen für die Controlling- und Unternehmenspraxis.
Planning adaptive learning paths for students’ progress throughout a course can be a challenging task, although it can be helpful for their learning progress. Within the HASKI-System, students should be able to get their own, personalized learning paths. In this paper, we present an approach towards the learning path sequencing problem. This idea is based on a novel proposal for arranging learning objects in a multi-dimensional space, bringing the relationship and similarities of these objects into a new relationship. We show, that we can use both, the Ant Colony Optimization Algorithm and the Genetic Algorithm with the idea of the Traveling-Salesman-Problem and get results, that are comparable with a proposed literature-based adaption mechanism. Nevertheless, the learning paths are all personalized based on the Felder & Silverman Learning Style Model and the hyperspace model will allow us later on to include more dimensions for other influencing factors.
Learning Analytics Dashboard for Educators: Proposed Project to Design with Pedagogical Background
(2023)
In this article, the authors describe a prototype of a Learning Analytics Dashboard (LAD) for educators. It is based on the analysis of pedagogical actions and taking into the process and learning style of students in an online environment based on learning analytics (LA). A description of the Dashboard structure, divided into levels and categories based on available learning analytics, will allow the educator to dive deeper into the online course themselves and explore more. It will also allow them to determine the level of student performance, identify gaps in learning materials, and research student data.
The authors have identified further directions for the development of a LAD for a professor, including modeling algorithms for researching student behavior and learning style using Artificial Intelligence and presenting LA in a visualized form.
This paper shows the stages of creating a professor's LAD prototype as a functional part of the adaptive learning system in the HASKI-System to analyze visual information obtained from LA and the possibilities to monitor the learning process, learning progress, student activity, and make decisions on careful intervention in the students’ learning process.
This article discusses the process of the laser turning of rotational symmetric, cylindrical components using ultrashort laser pulses with respect to the geometrical conditions and the resulting energy distribution during the laser turning process. As a result, process predictions and potential process optimizations are feasible. Particular attention is drawn to the laser spot formation on the cylindrical surface of the work piece in conjunction with the positioning of the laser beam relative to the rotation axis of the specimen. Based on fundamental calculations and experimental results, an optimum processing strategy is discussed, whereat the use of a trepanning optic in the laser turning process and the forming of a particular surface structure is additionally being issued.
Die fortschreitende Digitalisierung im Bildungssektor ist gerade für die Berufliche Weiterbildung von zentraler Bedeutung. Bereits seit längerer Zeit sind in diesem Kontext zeit- und ortsunabhängige Lehr-Lern-Formate sehr wichtig. Allerdings bietet digitales Lernen auch großes Potential hinsichtlich dem Thema Motivationsförderung. Serious Games stellen ein vielversprechendes Beispiel von Game-based Learning (GBL) dar, um die Lernmotivation zu adressieren. Hierbei geht es darum, Mechaniken (digitaler) Spiele zu verwenden, um einen Wissenserwerb zu ermöglichen, der zeitgleich motivierend gestaltet ist.
Das AdLer-Projekt hat zum Ziel ein frei verfügbares Instrument zu entwickeln, mit welchem Lehrende virtuelle 3D-Lernumgebungen erstellen und in ihrer Lehre in unterschiedlichen Formen einsetzen können. Mit Hilfe eines Autorentools werden Lehrende bei der Konzeption und Erstellung von 3D-Lernumgebungen unterstützt. Dadurch können Lehrende auf schnellem und einfachem Weg eigene digitale 3D-Lernumgebungen erstellen sowie nach ihren individuellen Vorstellungen hinsichtlich der eigenen Lernszenarien konfigurieren.
In der 3D-Lernumgebung können Lernende nach den Prinzipien des GBL erkundungsorientiert und interaktiv lernen. Dazu können Lernende mit einem virtuellen Avatar die AdLer-3D-Lernumgebung betreten und verschiedene Handlungsmöglichkeiten wahrnehmen. Sie können sich frei in dieser Lernumgebung bewegen und unterschiedliche Lernangebote (z.B. die Bearbeitung von Lernelementen, wie H5P, Text und Video) durchführen.
Im geplanten Poster-Pitch wird eine aktuelle Version der Software AdLer präsentiert und steht für die Konferenzteilnehmer zum Ausprobieren bereit.
The creation of aligning layers for the uniform orientation of liquid crystals is significant for both research and the application of liquid crystals. For all applications, the creation of aligning layers possessing controllable characteristics such as azimuthal and polar anchoring energies, easy-axis of director alignment and pretilt angle, in the same way as it is achieved by using photoaligning layers processed by light, is very important. Here, aligning properties of hybrid aligning layers created on the basis of sapphire surfaces additionally coated by photoaligning layer of PVCN-F are studied. These hybrid layers possess the properties of the nano-structured sapphire layer and the photosensitive PVCN-F layer, and complement each other. The irradiation time dependence of the azimuthal anchoring energy of the hybrid layers is studied. By using certain experimental conditions during irradiation of hybrid layers, e.g., polarization of light and irradiation time, a minimum value of the azimuthal anchoring energy, close to zero, was obtained. Atomic force microscope studies of the irradiated hybrid layers were also carried out. It was found that the behavior of the contact angle of nematic droplets placed on treated sapphire surfaces are in good agreement with properties of hybrid aligning layers and parameters of structuring surface obtained from AFM images.
Development of an Authoring Tool for the Creation of Individual 3D Game-Based Learning Environments
(2023)
Game-based learning in general and serious games in particular have a promising potential in higher education. In this article we going to show the capability of serious games with regard to current challenges in higher education. The focus of this article is the presentation of the AdLer authoring tool, which offers lecturers the possibility to design and generate virtual 3D learning environments
in which students can interact with learning content according to the principles of game-based learning.
BGB AT
(2023)
Schuldrecht AT
(2023)
We report on a comprehensive study of the mechanical properties of maraging steel
body-centred cubic lattice structures fabricated by a hybrid additive manufacturing technology that combines laser powder bed fusion with in situ high-speed milling. As the mechanical properties of additive manufactured components are inferior to, e.g., cast components, surface modifications can improve the mechanical behaviour. Different hybrid additive manufacturing technologies have been designed using additive and subtractive processes, improving process quality. Following this, mechanical testing is performed with respect to static tensile properties and dynamic stress, hardness, and porosity, comparing specimens manufactured by laser powder bed fusion only to those manufactured by the hybrid approach. In addition, the influence of different heat-treatment techniques on the mechanical behaviour of the lattice structures is investigated, namely solution and aging treatment as well as hot isostatic pressing. Thus, the influence of the superior surface quality due to the hybrid approach is evaluated, leading to, e.g., an offset of about 14–16% for the static testing of HIP lattice structures. Furthermore, the dynamic load behaviour can be improved with a finished surface, heading to a shift of the different zones of fatigue behaviour in the testing of hybrid-built specimens.
Two different control concepts for the automatic track guidance of forklifts are
proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial
Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks.
Axicon generated Bessel beams are a popular tool for high aspect ratio precision laser drilling. Spot diameter and working distance are given by the geometric parameters of the axicon and the wavelength used. Thus, it is difficult to manipulate the beam shape of a Bessel beam for a given setup. Spatial light modulators (SLMs) overcome limitations in flexibility. However, due to the limited phase shift of SLMs, only Bessel beams with flat cone angles and large focal length can be generated. In this contribution, an approach for generating Bessel beams with a shorter, but tunable focal length is presented. A physical axicon was combined with an SLM. A holographic image of a negative axicon is put on the SLM to generate a ring beam, which is focused by a subsequent physical axicon to get a small focal diameter. Thus, different sized high aspect ratio micro holes can be drilled without using any moving components.
We report on laser drilling borehole arrays using ultrashort pulsed lasers with a particular focus on reducing the inadvertent heat accumulation across the workpiece by optimizing the drilling sequence. For the optimization, evolutionary algorithms are used and their results are verified by thermal simulation using Comsol and experimentally evaluated using a thermal imaging camera. To enhance process efficiency in terms of boreholes drilled per second, multi-spot approaches are employed using a spatial light modulator. However, as higher temperatures occur across the workpiece when using simultaneous multi-spot drilling as compared to a single-spot process, a subtle spatial distribution and sequence of the multi-spot approach has to be selected in order to limit the
resulting local heat input over the processing time. Different optimization approaches based on evolutionary algorithms aid to select those drilling sequences which allow for the combination of a high efficiency of multi-spot profiles, a low-generated process temperature and a high-component quality. In particular, using a 4 x 4 laser spot array allows for the drilling of 40,000 boreholes in less than 76 s (526 boreholes/s) with a reduced temperature increase by about 35%, as compared to a single spot process when employing an optimized drilling sequence.
Krisen gab es schon immer. Aktuell hat sich jedoch ein ganzes Spektrum an Krisen entfaltet – es gibt politische und militärische Krisen, es brechen Energiemärke und Lieferketten weg, wir sehen ganze Branchen gefährdet, auch die Gesundheit macht uns Sorgen, vom Klimawandel ganz zu schweigen. In den Veranstaltungen der Ringvorlesung „Krisen und Auswege“, die im Wintersemester 2022/2023 stattfand, wurden Phänomene, Konzepte und Lösungen aus vielfältiger Perspektive erschlossen und damit die Komplexität des Gegenstands aufgezeigt. Herausforderungen für Unternehmen und Privatpersonen wurden identifiziert sowie der Einfluss auf technologische und organisatorische Entwicklungswege dargelegt.
In dieser Publikation wurden die einzelnen Vorträge der Ringvorlesung von den Autorinnen zusammengefasst.
Multi-Bessel Beams Generated by an Axicon and a Spatial Light Modulator for Drilling Applications
(2023)
We report on an optical setup to generate multi-Bessel beam profiles combining a refractive axicon and a spatial light modulator. Based on their particular beam profile, Bessel beams offer advantageous properties for micro drilling processes and internal volume processing, especially for transparent materials. In addition, the laser power of industrial, ultrashort pulsed lasers has increased significantly over the last few years, offering the possibility for highly efficient processes using multi-spot profiles. Our optical concept combines the dynamic possibilities of beam splitting using a spatial light modulator with the benefits of Bessel beams, which facilitates multi-Bessel beam processing. Beside the simulation and experimental evaluation of the generated multi-Bessel beams, we exemplify the applicability of the developed module for the perforation of thin metal foils by micro drilling.
Der technische Fortschritt von IT-Systemen, wie zum Beispiel der sogenannten „Künstlichen Intelligenz“ (KI) oder den „Common Data Spaces“ (CDSs), bringt die Frage nach der Akzeptanz dieser Systeme mit sich. Sowohl die aktive (ökonomische) Akzeptanz in Form des Erwerbs und der Nutzung, als auch die passive (soziale) Akzeptanz in Form der Duldung der aktiven Nutzung durch Dritte, sind für den Erfolg von IT-Systemen ausschlaggebend. Eine geringe Akzeptanz der Systeme und ein mangelndes Vertrauen in dieselben wird in einen Zusammenhang gebracht: Man fragt nach der Gestaltung „zuverlässiger und vertrauenswürdiger Systeme“, da diese besonders akzeptiert werden.
In diesem Beitrag werden „Vertrauen“ und dessen Zusammenhang mit „Zuverlässigkeit“ und „Verstehen“ im Kontext von IT-Systemen erörtert.
Im Sommer des Jahres 2022 hatte der Bayerische Landtag das neue Hochschulinnovationsgesetz (BayHIG) verabschiedet. Darin ist erstmals der Wissenstransfer als die dritte, neue Säule der Hochschulaufgaben enthalten.
Tatsächlich hat sich das IMI bereits seit 15 Jahren dem Wissenstransfer Hochschule - Wirtschaft verschrieben und zahlreiche Projekte mit unterschiedlichem Fokus erfolgreich durchgeführt. In der vorliegenden Publikation werden die bearbeiteten Themen im Wissenstransfer, die zahlreichen verschiedenen Veranstaltungsformate sowie die Drittmittelprojekte beleuchtet.
Metallic coatings are essential for numerous optical systems due to their high and broadband refl ectivity in the infrared spectral range. In contrast to well-established (protected)silver and gold mirror coatings, iridium is environmentally durable, referring to ISO 9211-3 and thermally stable up to 600 °C even without protective layers, as demonstrated. Additionally,the optical and related structural properties of atomic layer deposited (ALD) and magnetron sputtered (MS) Ir coatings were investigated using spectrophotometry, FTIR, ellipsometry, WLI, AFM, XRR, XRD, SEM, and electrical resistivity measurements. The properties of Ir ALD and Ir MS coatings diff er due to their topography and microstructure.
Metallic coatings are essential for numerous optical systems due to their high and broadband refl ectivity in the infrared spectral range. In contrast to well-established (protected)silver and gold mirror coatings, iridium is environmentally durable, referring to ISO 9211-3 and thermally stable up to 600 °C even without protective layers, as demonstrated. Additionally,the optical and related structural properties of atomic layer deposited (ALD) and magnetron sputtered (MS) Ir coatings were investigated using spectrophotometry, FTIR, ellipsometry, WLI,AFM, XRR, XRD, SEM, and electrical resistivity measurements. The properties of Ir ALD and Ir MS coatings diff er due to their topography and microstructure.
Abstract: This contribution demonstrates photonic crystal waveguides generated within bulk planar sapphire substrates. A femtosecond laser is used to modify the refractive index in a hexagonal pattern around the pristine waveguide core. Near-field measurements reveal single-mode behavior at a wavelength of 1550 nm and the possibility to adapt the mode-field diameter. Based on farfield examinations, the effective refractive index contrast between the pristine waveguide core and depressed cladding is estimated to 3x10-4. Additionally, Bragg gratings are generated within the waveguide core. Due to the inherent birefringence of Al2O3, the gratings exhibit two distinct wavelengths of main reflection. Each reflection peak exhibits a narrow spectral full width at a half maximum of 130 pm and can be selectively addressed by exciting the birefringent waveguide with appropriately polarized light. Furthermore, a waveguide attenuation of 1 dB cm-1 is determined.
We report on milling and tool wear characteristics of hybrid additive manufacturing comprising laser powder bed fusion and in situ high-speed milling, a particular process in which the cutter mills inside the powder bed without any cooling lubricant being applicable. Flank wear is found to be the dominant wear characteristic with its temporal evolution over utilization period revealing the typical s-shaped dependence. The flank wear land width is measured by microscopy and correlated to the achievable surface roughness of milled 3D-printed parts, showing that for flank wear levels up to 100 μm a superior surface roughness below 3 μm is accessible for hybrid additive manufacturing. Further, based on this correlation recommended tool, life scenarios can be deduced. In addition, by optimizing the finishing tool start position and the number of afore-built layers, the milling process is improved with respect to the maximum millable angle for undercut surfaces of 3D-printed parts to 30° for the roughing process and to 40° for the entire machining process including finishing
In this study, we analyse the advantageous effects of neural networks in combination with wavelet functions on the performance of financial market predictions. We implement different approaches in multiple experiments and test their predictive abilities with different financial time series. We demonstrate experimentally that both wavelet neural networks and neural networks with data pre-processed by wavelets outperform classical network topologies. However, the precision of conducted forecasts implementing neural network algorithms still propose potential for further refinement and enhancement. Hence, we discuss our findings, comparisons with “buy-and-hold” strategies and ethical considerations critically and elaborate on future prospects.
This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different
industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting
the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using
parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of
the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a
RL control concept, not considering time-variant parameters.
This paper presents a new control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significanlty improves the control quality. In order to demonstrate this, the new approach is compared to a RL control concept, not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.
Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by
the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here,
we used an EEG dataset to investigate the ability to automatically detect changes in brain
activity using machine learning and complex networks. Machine learning was applied at
three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of
the EEG time series, and (C) the complex network measures calculated from (B). Further, at
the abstraction level of (C), we developed new measures of complex networks relating to
community detection. As a result, the machine learning method was able to automatically
detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed
by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are
more important for the detection of ayahuasca. The most activated areas were the frontal
and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during
ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also
associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic
mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that
the dissemination of information in functional brain networks is slower when this drug is
present. Overall, our methodology was able to automatically detect changes in brain activity
during ayahuasca consumption and interpret how these psychedelics alter brain networks,
as well as provide insights into their mechanisms of action
In recent years, there has been an increasing interest in electrically conductive hydrogels for a wide range of biomedical applications, like tissue engineering or biosensors. In this study, we present a cost-effective conductive hydrogel based on alginate and graphene nanoplatelets for extrusion-based bioprinters. The hydrogel is prepared under ambient conditions avoiding high temperatures detrimental for cell culture environments. Investigation of the hydrogel revealed a conductivity of up to 7.5 S/cm, depending on the ratio of platelets. Furthermore, in vitro tests with human embyronic kidney cells - as an example cell type - showed good adhesion of the cells to the surface of the conductive hydrogel. Electrochemical measurements revealed a low electrode impedance which is desirable for the extracellular recording, but also low electrode capacitance, which is unfavorable for electrical stimulation purposes. Therefore, future experiments with the graphene nanoplatelets-based hydrogels will focus on electrodes for biosensors and extracellular recordings of neurons or cardiac myocytes.
Functional connectivity and convolutional neural networks for automatic classification of EEG data
(2022)
Objective: Tau ablation has a protective effect in epilepsy due to inhibition of the hyperexcitability/hypersynchrony. Protection may also occur in transgenic models of Alzheimer's disease by reducing the epileptic activity and normalizing the excitation/inhibition imbalance. However, it is difficult to determine the exact functions of tau, because tau knockout (tauKO) brain networks exhibit elusive phenotypes. In this study, we aimed to further explore the physiological role of tau using brain network remodeling. Approach: The effect of tau ablation was investigated in hippocampal-entorhinal slice co-cultures during network remodeling. We recorded the spontaneous extracellular neuronal activity over two weeks in single-slice cultures and co-cultures from control and tauKO mice. We compared the burst parameters and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity. Main results: Comparison of the control and tauKO co-cultures revealed that tau ablation had an anti-synchrony effect on the hippocampal-entorhinal two-slice networks at late stages of culture, in line with the literature. Differences were also found between the single-slice and co-culture conditions, which indicated that tau ablation had differential effects at the sub-network scale. For instance, tau ablation was found to have an anti-synchrony effect on the co-cultured hippocampal slices throughout the culture, possibly due to a reduction in the excitation/inhibition ratio. Conversely, tau ablation led to increased synchrony in the entorhinal slices at early stages of the co-culture, possibly due to homogenization of the connectivity distribution. Significance: The new methodology presented here proved useful for investigating the role of tau in the remodeling of complex brain-derived neural networks. The results confirm previous findings and hypotheses concerning the effects of tau ablation on neural networks. Moreover, the results suggest, for the first time, that tau has multifaceted roles that vary in different brain sub-networks.
Mental disorders are among the leading causes of disability worldwide. The first step in treating
these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a
possible solution to this problem, as we describe in this work. We present a method for the
automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG
time series and deep learning. We show that our approach can classify patients with Alzheimer’s
disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases,
that use raw EEG time series, shows that our method provides the highest precision. Therefore, the
application of deep neural networks on data from brain connections is a very promising method
for the diagnosis of neurological disorders.