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Social-Media-Content - Auswirkungen auf Fear of Missing Out und den Selbstwert junger Nutzer*innen
(2023)
Social-Media-Marketing ist ein wichtiger Baustein einer erfolgreichen Content-Strategie. Insbesondere jüngere Zielgruppen sind auf Social Media anzutreffen – und das oftmals über viele Stunden täglich. Neben den Vorteilen, die Social Media den Nutzer*innen bietet, gibt es aber auch Schattenseiten. Zwei negative Aspekte, die sogenannte Fear of Missing Out und ein verminderter Selbstwert, wurden im Frühjahr 2022 in einer empirischen Befragung von 1338 Personen zwischen 14 und 30 Jahren untersucht. Daneben wurden auch Daten zum grundsätzlichen Social-Media-Nutzungsverhalten erhoben. Die zentralen Erkenntnisse, die sich aus der Studie ableiten, werden in diesem Kapitel vorgestellt und mit Bezug auf ihre Relevanz für das Content-Marketing hin eingeordnet.
In diesem Beitrag werden die psychologischen Hintergründe und Wirkungsweisen des Content-Marketing betrachtet. Nach einer kurzen Einführung in die Thematik wird zuerst das für das weitere Verständnis notwendige psychologische Basiswissen vermittelt. Darauf bezugnehmend wird die allgemeine Wirkungsweise von Content-Marketing beleuchtet. Die Sichtweise wird dann für die letzten beiden Kapitel umgedreht und die beschriebenen psychologischen Faktoren dazu genutzt, um Anwender bei der Wahl der Content-Marketing-Inhalte und zuletzt bei der konkreten Ausgestaltung zu unterstützen.
Die meisten Effekte, die durch Content-Marketing hervorgerufen werden, funktionieren im B2C- oder B2B-Bereich durch das Ansprechen von Bedürfnissen, Interessen und Emotionen sowie die recht freien Entscheidungsmöglichkeiten der Adressaten. Im B2B‑Bereich werden ebenfalls Menschen mit Bedürfnissen, Interessen und Emotionen angesprochen, jedoch vorrangig beruflicher Natur, sodass in der Ausgestaltung geringfügige Unterschiede gemacht werden müssen.
Verfassen guter Texte
(2023)
Wer Texte für seinen Internetauftritt schreibt, möchte, dass diese auch gelesen werden. Doch Lesende sind ungeduldig, insbesondere am Monitor. Fasziniert man sie nicht in den ersten Sekunden, springen sie ab. Erfahren Sie hier, welche stilistischen Regeln Journalistinnen und Journalisten nutzen, um die Aufmerksamkeit ihrer Leser- oder Hörerschaft zu gewinnen und Texte mit wenig Aufwand zu perfektionieren. Ein paar Besonderheiten gelten auch für den Aufbau. Ein Schwerpunkt des Kapitels liegt auf dem Teaser, den ersten Zeilen, die in den Text locken sollen, sowie der Headline. Häufig ist es jedoch nicht der Text, der die Aufmerksamkeit der User fesselt, sondern ein Foto, idealerweise mit einer informativen Bildunterschrift. Zahlreiche Beispiele aus dem journalistischen Alltag machen das Beschriebene anschaulich. Als Zugabe informiert die Autorin Sie über die Bedeutung des Nutzwerts und attraktive Anlässe für eine Veröffentlichung.
Content-Marketing
(2023)
Content-Marketing, also die Planung, Produktion und Distribution von zielgruppen-adäquaten Inhalten, hat insbesondere durch Social Media nochmals an Bedeutung gewonnen. Im Hinblick auf die enorme Menge an Inhalten, die auf Nutzer konstant einwirken, ist es für Unternehmen immer schwieriger, die Aufmerksamkeit der Nutzer zu gewinnen. Nur Inhalte, die den Wünschen der Nutzer entsprechen und diesen in irgendeiner Form einen Mehrwert bieten, haben die Chance, zur Erfüllung von Kommunikationszielen von Unternehmen beizutragen. Die Bereitstellung derartiger Inhalte setzt einen sinnvollen (Planungs-)Prozess voraus. Das vorliegende Buch bietet Praktikern und Studierenden einen Überblick über die verschiedenen Bereiche eines Content-Marketing.
Vor dem Hintergrund einer zunehmenden Informations- und Reizüberlastung der Konsumenten werden aus Unternehmenssicht zielgruppenadäquate Inhalte, insbesondere zur Erreichung von kommunikationspolitischen Zielsetzungen, immer wichtiger. Um diese zu gewährleisten, bedarf es einer sinnvollen Planung, Produktion und Distribution von Inhalten. Der vorliegende Beitrag gibt einen Überblick über einen solchen Prozess und veranschaulicht die notwendigen Schritte für ein erfolgreiches Content-Marketing.
Electrochemical pressure impedance spectroscopy (EPIS) has received the attention of researchers as a method to study mass transport processes in polymer electrolyte mem-brane fuel cells (PEMFC). It is based on analyzing the cell voltage response to a harmonic excitation of the gas phase pressure in the frequency domain. Several experiments with a single-cell fuel cell have shown that the spectra contain information in the frequency range typical for mass transport processes and are sensitive to specific operating condi-tions and structural fuel cell parameters. To further benefit from the observed features, it is essential to identify why they occur, which to date has not yet been accomplished. The aim of the present work, therefore, is to identify causal links between internal processes and the corresponding EPIS features.
To this end, the study follows a model-based approach, which allows the analysis of inter-nal states that are not experimentally accessible. The PEMFC model is a pseudo-2D model, which connects the mass transport along the gas channel with the mass transport through the membrane electrode assembly. A modeling novelty is the consideration of the gas vol-ume inside the humidifier upstream the fuel cell inlet, which proves to be crucial for the reproduction of EPIS. The PEMFC model is parametrized to a 100 cm² single cell of the French project partner, who provided the experimental EPIS results reproduced and in-terpreted in the present study.
The simulated EPIS results show a good agreement with the experiments at current den-sities ≤ 0.4 A cm–2, where they allow a further analysis of the observed features. At the lowest excitation frequency of 1 mHz, the dynamic cell voltage response approaches the static pressure-voltage response. In the simulated frequency range between 1 mHz – 100 Hz, the cell voltage oscillation is found to strongly correlate with the partial pressure oscillation of oxygen, whereas the influence of the water pressure is limited to the low frequency region.
The two prominent EPIS features, namely the strong increase of the cell voltage oscillation and the increase of phase shift with frequency, can be traced back via the oxygen pressure to the oscillation of the inlet flow rate. The phenomenon of the oscillating inlet flow rate is a consequence of the pressure change of the gas phase inside the humidifier and in-creases with frequency. This important finding enables the interpretation of experimen-tally observed EPIS trends for a variation of operational and structural fuel cell parame-ters by tracing them back to the influence of the oscillating inlet flow rate.
The separate simulation of the time-dependent processes of the PEMFC model through model reduction shows their individual influence on EPIS. The sluggish process of the wa-ter uptake by the membrane is visible below 0.1 Hz, while the charge and discharge of the double layer becomes visible above 1 Hz. The gas transport through the gas diffusion layer is only visible above 100 Hz. The simulation of the gas transport through the gas channel
without consideration of the humidifier becomes visible above 1 Hz. With consideration of the humidifier the gas transport through the gas channel is visible throughout the fre-quency range. The strong similarity of the spectra considering the humidifier with the spectra of the full model setup shows the dominant influence of the humidifier on EPIS.
A promising observation is the change in the amplitude relationship between the cell volt-age and the oxygen partial pressure oscillation as a function of the oxygen concentration in the catalyst layer. At a frequency where the influence of oxygen pressure on the cell voltage is dominant, for example at 1 Hz, the amplitude of the cell voltage oscillation could be used to indirectly measure the oxygen concentration in the catalyst layer.
This article presents the development, parameterization, and experimental validation of a pseudo-three-dimensional (P3D) multiphysics aging model of a 500 mAh high-energy lithium-ion pouch cell with graphite negative electrode and lithium nickel manganese cobalt oxide (NMC) positive electrode. This model includes electrochemical reactions for solid electrolyte interphase (SEI) formation at the graphite negative electrode, lithium plating, and SEI formation on plated lithium. The thermodynamics of the aging reactions are modeled depending on temperature and ion concentration and the reactions kinetics are described with an Arrhenius-type rate law. Good agreement of model predictions with galvanostatic charge/discharge measurements and electrochemical impedance spectroscopy is observed over a wide range of operating conditions. The model allows to quantify capacity loss due to cycling near beginning-of-life as function of operating conditions and the visualization of aging colormaps as function of both temperature and C-rate (0.05 to 2 C charge and discharge, −20 °C to 60 °C). The model predictions are also qualitatively verified through voltage relaxation, cell expansion and cell cycling measurements. Based on this full model, six different aging indicators for determination of the limits of fast charging are derived from post-processing simulations of a reduced, pseudo-two-dimensional isothermal model without aging mechanisms. The most successful aging indicator, compared to results from the full model, is based on combined lithium plating and SEI kinetics calculated from battery states available in the reduced model. This methodology is applicable to standard pseudo-two-dimensional models available today both commercially and as open source.
Variable refrigerant flow (VRF) and variable air volume (VAV) systems are considered among the best heating, ventilation, and air conditioning systems (HVAC) thanks to their ability to provide cooling and heating in different thermal zones of the same building. As well as their ability to recover the heat rejected from spaces requiring cooling and reuse it to heat another space. Nevertheless, at the same time, these systems are considered one of the most energy-consuming systems in the building. So, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. This study aims to compare these two energy systems by conducting an energy model simulation of a real building under a semi-arid climate for cooling and heating periods. The developed building energy model (BEM) was validated and calibrated using measured and simulated indoor air temperature and energy consumption data. The study aims to evaluate the effect of these HVAC systems on energy consumption and the indoor thermal comfort of the building. The numerical model was based on the Energy Plus simulation engine. The approach used in this paper has allowed us to reach significant quantitative energy saving along with a high level of indoor thermal comfort by using the VRF system compared to the VAV system. The findings prove that the VRF system provides 46.18% of the annual total heating energy savings and 6.14% of the annual cooling and ventilation energy savings compared to the VAV system.
During pyrolysis, biomass is carbonised in the absence of oxygen to produce biochar with heat and/or electricity as co-products making pyrolysis one of the promising negative emission technologies to reach climate goals worldwide. This paper presents a simplified representation of pyrolysis and analyses the impact of this technology on the energy system. Results show that the use of pyrolysis can allow getting zero emissions with lower costs by making changes in the unit commitment of the power plants, e.g. conventional power plants are used differently, as the emissions will be compensated by biochar. Additionally, the process of pyrolysis can enhance the flexibility of energy systems, as it shows a correlation between the electricity generated by pyrolysis and the hydrogen installation capacity, being hydrogen used less when pyrolysis appears. The results indicate that pyrolysis, which is available on the market, integrates well into the energy system with a promising potential to sequester carbon.
Digitalisierung ist heute allgegenwärtig. Wo im Privaten bereitwillig neue digitale Tools, Apps und Funktionen genutzt werden, tun sich Unternehmen in der Umsetzung von Digitalisierungsprojekten oft schwer. Dieser Beitrag beleuchtet die Motive für Digitalisierungsvorhaben, ihre Hürden sowie die Auswirkung auf die Arbeitsbelastung von Mitarbeitenden und versucht in der Verknüpfung mit den Grundprinzipien des Kontinuierlichen Verbesserungsprozesses, Handlungsempfehlungen für eine erfolgreiche Umsetzung dieser abzuleiten.
Device and method for monitoring and optimising a temporal trigger stability (WO2023094554A1)
(2023)
The present invention relates to devices for monitoring and optimising a temporal trigger stability of an extracorporeal circulatory support means, and to open-loop and closed-loop control units for the extracorporeal circulatory support means comprising such a device, and to corresponding methods. A device (10) for monitoring a temporal trigger stability of an extracorporeal circulatory support means is accordingly proposed, which device is designed to receive a first dataset (14) of a measurement of an ECG signal of a supported patient over a predefined period of time. The device (10) comprises an evaluation unit (16), which is designed to determine or identify a plurality of R triggers (26) from the first dataset (14), wherein the evaluation unit (16) is also designed to receive or provide a second dataset (20) having evaluated ECG signals and a plurality of R triggers (28) and to selectively map the second dataset (20) on the first dataset (14). The device is also designed to emit a signal (22) that characterises a temporal gap between successive R triggers (26) from the first dataset (14) and successive R triggers (28) from the second dataset (20) which are mapped on the first dataset.
Unterschiedliche Stimulationszeitpunkte bei bimodaler Versorgung mit Hörgerät und Cochleaimplantat
(2023)
Die bimodale Versorgung von Patienten mit Hörgerät (HG) ipsilateral und Cochleaimplantat (CI) kontralateral bei asymmetrischem Hörverlust ist aufgrund vieler inhärenter Variablen die komplizierteste Versorgungsart im Kontext der Versorgung mit CI. Im vorliegenden Übersichtsartikel werden alle systematischen interauralen Unterschiede zwischen elektrischer und akustischer Stimulation dargestellt, die bei dieser Versorgungsart auftreten können. Darüber hinaus werden Methoden zur Quantifizierung des interauralen Latenzoffsets, also des Zeitunterschieds zwischen der akustischen und elektrischen Stimulation des Hörnervs, mittels Registrierung auditorisch evozierter Potenziale – erzeugt durch akustische bzw. elektrische Stimulation – und Messungen an den Sprachprozessoren und Hörgeräten vorgestellt. Die technische Kompensation des interauralen Latenzoffsets und ihre positive Auswirkung auf die Schalllokalisationsfähigkeit bimodal mit CI und HG versorgter Patienten wird ebenfalls beschrieben. Zuletzt werden neueste Erkenntnisse diskutiert, die Gründe dafür aufzeigen, warum die Kompensation des interauralen Latenzoffsets das Sprachverstehen im Störgeräusch bei bimodal versorgten CI-/HG-Trägern nicht verbessert.
eLetter zum Artikel "Condiciones neuropsi-quiátricas y probable causa de muerte de Maurice Ravel" von Gómez-Carvajal AM, Botero-Meneses JS, Palacios-Espinosa X und Palacios-Sánchez L., veröffentlicht in Iatreia 35(3), Seite 341-8 (DOI: https://doi.org/10.17533/udea.iatreia.154).
Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In the past years, there has been a remarkable increase of machine-learning-based solutions that have addressed the aforementioned issues. In particular, deep-learning practitioners have usually relied on heavily fine-tuned, customized discriminative algorithms. Although, these methods can provide solid results, they seem to lack semantic understanding of the provided data. Motivated by this limitation, in this work, we employ a generative solution, as it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for three seismic applications: demultiple, denoising and interpolation. To that end, we run experiments on synthetic and on real data, and we compare the diffusion performance with standardized algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.
In Zeiten großer Veränderungen haben genossenschaftlich organisierte KMU die Möglichkeit, auf komplexe Herausforderungen mit kooperativen Lösungsansätzen zu reagieren, vor allem wenn dabei die Kraft und Kreativität der Gemeinschaft genutzt wird. Getreu dem Motto „Was einer alleine nicht schafft, das schaffen viele“ des Genossenschaftsvorreiters Friedrich Wilhelm Raiffeisen ist gemeinschaftliches unternehmerisches Handeln identitätsstiftend und motivierend, woraus wiederum eine sich selbst verstärkende Eigendynamik entstehen kann. Wie Mittelstand, Politik und Gesellschaft davon profitieren, stellen Prof. Dr. Tobias Popovic und Prof. Dr. Thomas Baumgärtler in diesem Beitrag dar.
Skin cancer detection proves to be complicated and highly dependent on the examiner’s skills. Millimeter-wave technologies seem to be a promising aid for the detection of skin cancer. The different water content of the skin area affected by cancer compared to healthy skin changes its reflective property. Due to limited available resources on the dielectric properties of skin cancer, especially in comparison to surrounding healthy skin, accurate simulations and evaluations are quite challenging. Therefore, comparing different results for different approaches and starting points can be difficult. In this paper, the Effective Medium Theory is applied to model skin cancer, which provides permittivity values dependent on the water content.
A method for evaluating skin cancer detection based on millimeter-wave technologies is presented. For this purpose, the relative permittivities are calculated using the effective medium theory for the benign and cancerous lesion, considering the change in water content between them. These calculated relative permittivities are further used for the simulation and evaluation of skin cancer detection using a substrate-integrated waveguide probe. A difference in the simulated scattering parameters S 11 of up to 13dB between healthy and cancerous skin can be determined in the best-case.
Investigation on Bowtie Antennas Operating at Very Low Frequencies for Ground Penetrating Radar
(2023)
The efficiency of Ground Penetrating Radar (GPR) systems significantly depends on the antenna performance as the signal has to propagate through lossy and inhomogeneous media. GPR antennas should have a low operating frequency for greater penetration depth, high gain and efficiency to increase the receiving power and should be compact and lightweight for ease of GPR surveying. In this paper, two different designs of Bowtie antennas operating at very low frequencies are proposed and analyzed.
The Transport Layer Security protocol is a widespread cryptographic protocol designed to provide secure communication over insecure networks by providing authenticity, integrity, and confidentiality. As a first step, in the TLS Handshake Protocol a common master secret is negotiated. In many configurations, this step makes considerable use of asymmetric cryptographic algorithms. It seems to be a prevalent assumption that the use of such asymmetric cryptographic algorithms is unsuitable for resource-constrained devices. Therefore, the work at hand analyzes the runtime performance of the TLS vl.2 session establishments on an embedded ARM Cortex-M4 platform. We measure the execution time to generate and parse session establishment messages for the client and server sides. In particular, we study the impact of different elliptic curves used for the ephemeral Diffie-Hellman key exchange and the impact of different lengths and subject public key algorithms of certification paths. Our analysis shows that the use of asymmetric cryptographic algorithms is well possible on resource-constrained devices, if carefully chosen and well implemented. This allows the use of the well-proven TLS protocol also for applications from the (Industrial) Internet of Things, including Fieldbus communication.
This paper presents a system that uses a multi-stage AI analysis method for determining the condition and status of bicycle paths using machine learning methods. The approach for analyzing bicycle paths includes three stages of analysis: detection of the road surface, investigation of the condition of the bicycle paths, and identification of substrate characteristics. In this study, we focus on the first stage of the analysis. This approach employs a low-threshold data collection method using smartphone-generated video data for image recognition, in order to automatically capture and classify surface condition and status.
For the analysis convolutional neural networks (CNN) are employed. CNNs have proven to be effective in image recognition tasks and are particularly well-suited for analyzing the surface condition of bicycle paths, as they can identify patterns and features in images. By training the CNN on a large dataset of images with known surface conditions, the network can learn to identify common features and patterns and reliably classify them.
The results of the analysis are then displayed on digital maps and can be utilized in areas such as bicycle logistics, route planning, and maintenance. This can improve safety and comfort for cyclists while promoting cycling as a mode of transportation. It can also assist authorities in maintaining and optimizing bicycle paths, leading to more sustainable and efficient transportation system.
In recent times, 5G has found applications in several public as well as private networks. There is a growing need to make it compatible with diverse services without compromising security. Current security options for authenticating devices into a home network are 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP)-AKA'. However, for specific use cases such as private networks, more customizable and convenient authentication mechanisms are required. The current mobile networks use authentication based only on SIM cards, but as 5G is being applied in fields like IIoT and automation, even in Non-Public-Networks (NPNs), there is a need for a simpler method of authentication. Certificate-based authentication is one such mechanism that is passwordless and works solely on the information present in the digital certificate that the user holds. The paper suggests an authentication mechanism that performs certificate-based mutual authentication between the UE and the Home network. The proposed concept identifies both the user and network with digital certificates and intends to carry out primary authentication with the help of it. In this work we conduct a study on presently available authentication protocols for 5G networks, both theoretically and experimentally in hardware as well as virtual environments. On the basis of the analysis a series of proposed steps for certificate primary authentication are presented.
The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on tiny, embedded devices such as low-power micro-controller units (MCUs). Sparse backpropagation algorithms try to reduce the computational load of on-device learning by training only a subset of the weights and biases. Existing approaches use a static number of weights to train. A poor choice of this so-called backpropagation ratio limits either the computational gain or can lead to severe accuracy losses. In this paper we present TinyProp, the first sparse backpropagation method that dynamically adapts the back-propagation ratio during on-device training for each training step. TinyProp induces a small calculation overhead to sort the elements of the gradient, which does not significantly impact the computational gains. TinyProp works particularly well on fine-tuning trained networks on MCUs, which is a typical use case for embedded applications. For typical datasets from three datasets MNIST, DCASE2020 and CIFAR10, we are 5 times faster compared to non-sparse training with an accuracy loss of on average 1%. On average, TinyProp is 2.9 times faster than existing, static sparse backpropagation algorithms and the accuracy loss is reduced on average by 6 % compared to a typical static setting of the back-propagation ratio.
The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps - detection and classification - are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28% was attained on real data obtained from a written exam.
As cyber-attacks and functional safety requirements increase in Operational Technology (OT), implementing security measures becomes crucial. The IEC/IEEE 60802 draft standard addresses the security convergence in Time-Sensitive Networks (TSN) for industrial automation.We present the standard’s security architecture and its goals to establish end-to-end security with resource access authorization in OT systems. We compare the standard to our abstract technology-independent model for the management of cryptographic credentials during the lifecycles of OT systems. Additionally, we implemented the processes, mechanisms, and protocols needed for IEC/IEEE 60802 and extended the architecture with public key infrastructure (PKI) functionalities to support complete security management processes.
In this paper we present the concept of the "KI-Labor Südbaden" to support regional companies in the use of AI technologies. The approach is based on the "Periodic Table of AI" and is extended with both new dimensions for sustainability, and the impact of AI on the working environment. It is illustrated on the basis of three real-world use cases: 1. The detection of humans with lowresolution infrared (IR) images for collaborative robotics; 2. The use of machine data from specifically designed vehicles; 3. State-of-the-art Large Language Models (LLMs) applied to internal company documents. We explain the use cases, thereby demonstrating how to apply the Periodic Table of AI to structure AI applications.
Im Zuge der Machbarkeitsstudie „BubbleMeth“ (FKZ BWFE310091) wurde die Machbarkeit der biologischen Methanisierung in einem neukonzipierten innovativen Pilot-Reaktor, basierend auf einer Gegenstromblasensäule mit separatem Entgasungs-Reaktor, sowohl für den Betrieb in der biologischen in-situ als auch der ex-situ Methanisierung demonstriert.
Die Pilot-Anlage besteht aus einer Gegenstromblasensäule und einem separaten Entgasungs-Reaktor und wurde an der Hochschule Offenburg geplant und gebaut. Die beiden Reaktor-Säulen haben jeweils eine Höhe von 10 m, einen Säulendurchmesser von 0,3 m und ein Gesamtreaktionsvolumen von etwa 1,1 m3. Der Gaseintrag erfolgt über Sinterplatten am Boden der Gegenstromblasensäule. In dieser Begasungssäule strömt die Flüssigkeit in entgegengesetzter Richtung zu den aufsteigenden Gasblasen und reichert sich durch den am Säulenfuß vorliegenden hydraulischen Druck zunehmend mit gelöstem Gas an. Die Säule, in die das Eduktgas am Säulenboden eingetragen wird, ist in Abbildung 1 auf der rechten Seite dargestellt und befindet sich auf der Saugseite einer Pumpe. Bei einer etwa 9,5 m hohen Wassersäule erhöht sich der Absolutdruck am Säulenboden auf etwa 1,95 bar, womit sich die Löslichkeit einer beliebigen Gaskomponente im Vergleich zum Atmosphärendruck bei konstanter Temperatur im Gleichgewicht gemäß dem Henry’schen Gesetz näherungsweise verdoppelt. Dieser Effekt wird genutzt, um die Verfügbarkeit von gelöstem Wasserstoff für die bei der biologischen Methanisierung katalytisch wirkenden hydrogenothrophen Archaeen zu erhöhen. Durch die Zirkulation der Flüssigkeit und den damit erreichten Druckwechsel wird auf der Seite des Entgasungs-Reaktors ein Ausgasen der relativ zum Atmosphärendruck übersättigten Gaskomponente ermöglicht. Durch die Zirkulation der Flüssigkeit über zwei Säulen wird außerdem die räumliche Trennung des Eduktgaseintrages und der Produktgasabtrennung erreicht.
Die in-situ Methanisierung wurde in der Machbarkeitsstudie bis zu einer organischen Beladungsrate von 0,94 kg m-3 d-1 realisiert. Die erwartete Biogasbildungsrate (BGBR) bei vollständiger Umsetzung des Glucose/Fructose-Substrates zu Methan und CO2 lag bei ca. 0,686 m3 m-3 d-1. Die gemessene BGBR erreichte 0,61 ± 0,03 m3 m-3 d-1. Die geringe Abweichung kann auf eine zusätzliche Nutzung des Substrates für den Erhaltungsstoffwechsel des gesamten biologischen Systems zurückgeführt werden. Der maximale volumetrische H2-Eintrag betrug während der in-situ Methanisierung 0,785 m3 m-3 d-1 und ist dabei bezogen auf das gesamte Reaktionsvolumen von ca. 1,1 m3 in beiden Reaktorkolonnen. Das eingesetzte H2:CO2-Verhältnis lag bei 2,3, um einen vollständigen CO2-Umsatz und eine damit verbundene Verschiebung des pH-Wertes in den alkalischen Bereich bei der in-situ Methanisierung zu vermeiden. Die Produktgaszusammensetzung lag stabil bei ca. 80 Vol.% CH4, 18 Vol.% CO2 und geringen Mengen an Stickstoff, die im Wesentlichen aus der manuellen Entnahme der Gasproben resultieren, und entsprach der erwarteten Zusammensetzung bei dem vorgegebenen H2:CO2-Verhältnis.
Im Anschluss an die Untersuchungsphase der in-situ Methanisierung wurde der Prozess auf die ex-situ Methanisierung umgestellt. Dazu wurde die OLR schrittweise reduziert und gleichzeitig der Eintrag von CO2 aus einer Druckgasflasche erhöht. Die ex-situ Methanisierung wurde im Rahmen der Machbarkeitsstudie bis zu einem volumenspezifischen CO2-Eintrag bezogen auf das Gesamtreaktionsvolumen von 1,1 m3 von 0,563 m3 CO2 m-3 d-1 durchgeführt.
Der maximale volumetrische H2-Eintrag betrug während der ex-situ Methanisierung 2,168 m3 m-3 d-1. Das eingesetzte H2:CO2-Verhältnis lag bei 3,6 bis 3,9. Die Produktgaszusammensetzung lag stabil bei ca. 91 Vol.% CH4, 8 Vol.% CO2 und geringen Mengen an Stickstoff, und entsprach der erwarteten Zusammensetzung bei dem vorgegebenen H2:CO2-Verhältnis.
Besonders bemerkenswert war, dass sowohl bei der in-situ als auch der ex-situ Methanisierung und den jeweils in der Machbarkeitsstudie eingesetzten maximalen volumetrischen H2-Einträge weder im austretenden Produktgas am Entgasungsreaktor noch im rezirkulierten Gas am Kopf des Begasungsreaktors Wasserstoff nachzuweisen war. Damit besteht großes Potenzial für eine weitere Steigerung der Methanbildungsrate. Aus diesem Grund sollen die Arbeiten zur biologischen Methanisierung in einem Innovationsprojekt fortgeführt werden. Die Anlage soll hinsichtlich ihrer Eignung in einer relevanten Einsatzumgebung zur Methanisierung von in Biogas enthaltenem CO2-bewertet werden. Dazu soll die Anlage außerdem mit einem preiswerten alkalischen Elektrolyseur kombiniert werden, um das Verfahren so kostengünstig wie möglich zu gestalten. Dieser Elektrolyseur soll in Anlehnung an die fluktuierende Energiebereitstellung Erneuerbarer Energien zyklisch betrieben werden und dabei vor allem zu Zeiten günstiger Spotmarktpreise in Betrieb sein.
Currently, immersive technologies are enjoying great popularity. This trend is reflected in technological advances and the emergence of new products for the mass market, such as augmented reality glasses. The range of applications for immersive technologies is growing with more efficient and affordable technologies and student adoption. Especially in education, the use will improve existing learning methods. Immersive application use visual, audio and haptic sensors to fully engage the user in a virtual environment. This impression is reinforced with the help of realistic visualizations and the opportunity for interaction. In particular, Augmented reality is characterized by a high degree of integration between reality and the inserted virtual objects. An augmented interactive simulation for the determination of the specific charge of an electron will be used as an example to demonstrate how such immersion can be created for users. A virtual Helmholtz coil is used to measure and calculate the e/m constant. The voltage at the cathode for generating the electron beam, but also the voltage of the homogeneous magnetic field for deflecting the electron beam, can be variably controlled by haptic user input. Based on these voltages, an immersive virtual electron beam is calculated and visualized. In this paper, the authors present the conceptual steps of this immersive application and address the challenges associated with designing and developing an augmented and interactive simulation.
Redesigning a curriculum for teaching media technology is a major challenge. Up-to-date teaching and learning concepts are necessary that meet the constant technological progress and prepare students specifically for their professional life. Teaching and studying should be characterized by a student-oriented teaching and learning culture. In order to achieve this goal, consistent evaluation is essential. The aim of the evaluation concept presented here is to generate structured information regarding the quality of content-related, didactic and organizational aspects of teaching. The exchange of opinions between students and lecturers should be encouraged in order to continuously improve the teaching and learning processes.
The paper will focus on the activities of the International Year of Light and Optical Technologies 2015 (IYL) with their impact in life, science, art, culture, education and outreach as well as the importance in promoting the objectives for sustainable development. It describes our activities carried out in the run-up to or during the IYL, as well as reports on the generic projects that led to the success of the IYL. The success of the IYL is illustrated by examples and statistics. Relating to the potential and success of the IYL, the impact and the genesis of the International Day of Light (IDL) is presented. Impressions from the opening ceremony of the IYL in Paris at UNESCO headquarters and the Inaugural Ceremony of the IDL will then be covered. A second focus is placed on the interdisciplinary media projects realized by the students of our university dedicated to these events. Finally, an analysis of the impact and legacy of IYL and IDL will be presented.
This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team’s main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in the manufacturing industry and optimising poorly or non-digitised industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed a curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI’s contributions to the field, emphasising the importance of resource efficiency and system optimisation in industrial settings. By addressing the challenges posed by under-digitised infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at a local level. A crucial element of the added value is the new channel for obtaining feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.
The article investigates the development of a manufacturing route for highly porous titanium foams suitable for craniofacial surgery applications, particularly in cranioplasties. The study focuses on the polyurethane replication method for foam production and emphasizes reducing residual gas content, as it significantly affects the mechanical properties and suitability for approval of the foams. Various factors such as starting materials, solvent debinding, heating schedules, and hydrogen atmosphere are analyzed for their impact on residual gas content. It is shown that significant reductions in residual gas content can only be achieved by reworking each step of the process. A combination of initial solvent debinding of the PU template with dimethyl sulphoxide, reduction of suspension additives, use of coarser Gd. 1 powders, and an integrated debinding and sintering process under partial hydrogen atmosphere achieves a significant reduction in residual gas content. This way, the potential for producing titanium foams that comply with relevant standards for craniofacial implants is demonstrated.
Bei der Vorrichtung zum Verdampfen einer Flüssigkeit ist eine offenporöse Struktur (1) in einem Gehäuse (8) angeordnet, die zumindest bereichsweise als eine Heizvorrichtung ausgebildet oder mittels einer externen Heizvorrichtung oder Bereiche der offenporösen Struktur (1) auf eine Temperatur, die mindestens der Siedetemperatur der jeweiligen Flüssigkeit entspricht, erwärmbar ist. Die Flüssigkeit ist in einem außerhalb des Gehäuses (8) angeordneten Reservoir (4), mit einer Menge aufgenommen, die während des Betriebs eine kontinuierliche Verdampfung von Flüssigkeit ermöglicht. Das Reservoir ist über mindestens eine Leitung (3) für Flüssigkeit mit dem Gehäuse (8) verbunden und in der mindestens einen Leitung (3) ist/sind ein Ventil und/oder eine Pumpe oder ein Verdichter (5) angeordnet. Die Leitung (3) mündet in mindestens eine Öffnung mindestens einer Düse (2) oder mindestens eine Austrittsöffnung und die Düse (2), deren Öffnung(en) und/oder die mindestens eine Austrittsöffnung der Leitung (3) ist/sind so angeordnet, dass Flüssigkeit auf Oberflächenbereiche der offenporösen Struktur (1) auftrifft und/oder in Poren der offenporösen Struktur (1) eintritt, wenn Flüssigkeit durch die eine Leitung (3) strömt.
Die vorliegende Erfindung betrifft ein Verfahren zum Herstellen dreidimensionaler Bauteile, bei dem aus einem pulverförmigen Ausgangswerkstoff (1), der mit mindestens zwei Komponenten gebildet ist, wobei die Komponenten einen unterschiedlichen Dampfdruck bei gleicher Temperatur aufweisen, durch ein additives Strahlfertigungsverfahren ein Bauteil hergestellt wird. Mindestens ein Prozessparameter zum Betrieb mindestens eines zweidimensional auslenkbaren Energiestrahls wird derart eingestellt, dass sich der Dampfdruck mindestens einer der Komponenten ändert, so dass der Anteil dieses chemischen Elements oder dieser Legierung im Bauteilvolumen lokal definiert im Bauteil variiert wird.
Method and system for extractin metal and oxygen from powdered metal oxides (EP000004170066A2)
(2023)
A method for extracting metal and oxygen from powdered metal oxides in electrolytic cell is proposed, the electrolytic cell comprising a container, a cathode, an anode and an oxygen-ion-conducting membrane, the method comprising providing a solid oxygen ion conducting electrolyte powder into a container, providing a feedstock comprising at least one metal oxide in powdered form into the container, applying an electric potential across the cathode and the anode, the cathode being in communication with the electrolyte powder and the anode being in communication with the membrane in communication with the electrolyte powder, such that at least one respective metallic species of the at least one metal oxide is reduced at the cathode and oxygen is oxidized at the anode to form molecular oxygen, wherein the potential across the cathode and the anode is greater than the dissociation potential of the at least one metal oxide and less than the dissociation potential of the solid electrolyte powder and the membrane.
Wireless communication networks are crucial for enabling megatrends like the Internet of Things (IoT) and Industry 4.0. However, testing these networks can be challenging due to the complex network topology and RF characteristics, requiring a multitude of scenarios to be tested. To address this challenge, the authors developed and extended an automated testbed called Automated Physical TestBed (APTB). This testbed provides the means to conduct controlled tests, analyze coexistence, emulate multiple propagation paths, and model dependable channel conditions. Additionally, the platform supports test automation to facilitate efficient and systematic experimentation. This paper describes the extended architecture, implementation, and performance evaluation of the APTB testbed. The APTB testbed provides a reliable and efficient solution for testing wireless communication networks under various scenarios. The implementation and performance verification of the testbed demonstrate its effectiveness and usefulness for researchers and industry practitioners.
Fused Filament Fabrication (FFF) is a widespread additive manufacturing technology, mostly in the field of printable polymers. The use of filaments filled with metal particles for the manufacture of metallic parts by FFF presents specific challenges regarding debinding and sintering. For aluminium and its alloys, the sintering temperature range overlaps with the temperature range of thermal decomposition of many commonly used “backbone” polymers, which provide stability to the green parts. Moreover, the high oxygen affinity of aluminium necessitates the use of special sintering regimes and alloying strategies. Therefore, it is challenging to achieve both low porosity and low levels of oxygen and carbon impurities at the same time. Feedstocks compatible with the special requirements of aluminium alloys were developed. We present results on the investigation of debinding/sintering regimes by Fourier Transform Infrared spectroscopy (FTIR) based In-Situ Process Gas Analysis and discuss optimized thermal treatment strategies for Al-based FFF.
This book constitutes the proceedings of the 23rd International TRIZ Future Conference on Towards AI-Aided Invention and Innovation, TFC 2023, which was held in Offenburg, Germany, during September 12–14, 2023. The event was sponsored by IFIP WG 5.4.
The 43 full papers presented in this book were carefully reviewed and selected from 80 submissions. The papers are divided into the following topical sections: AI and TRIZ; sustainable development; general vision of TRIZ; TRIZ impact in society; and TRIZ case studies.
Recent advances in spiked shoe design, characterized by increased longitudinal stiffness, thicker midsole foams, and reconfigured geometry are considered to improve sprint performance. However, so far there is no empirical data on the effects of advanced spikes technology on maximal sprinting speed (MSS) published yet. Consequently, we assessed MSS via ‘flying 30m’ sprints of 44 trained male (PR: 10.32 s - 12.08 s) and female (PR: 11.56 s - 14.18 s) athletes, wearing both traditional and advanced spikes in a randomized, repeated measures design. The results revealed a statistically significant increase in MSS by 1.21% on average when using advanced spikes technology. Notably, 87% of participants showed improved MSS with the use of advanced spikes. A cluster analysis unveiled that athletes with higher MSS may benefit to a greater extent. However, individual responses varied widely, suggesting the influence of multiple factors that need detailed exploration. Therefore, coaches and athletes are advised to interpret the promising performance enhancements cautiously and evaluate the appropriateness of the advanced spike technology for their athletes critically.
3D Bin Picking with an innovative powder filled gripper and a torque controlled collaborative robot
(2023)
A new and innovative powder filled gripper concept will be introduced to a process to pick parts out of a box without the use of a camera system which guides the robot to the part. The gripper is a combination of an inflatable skin, and a powder inside. In the unjammed condition, the powder is soft and can adjust to the geometry of the part which will be handled. By applying a vacuum to the inflatable skin, the powder gets jammed and transforms to a solid shaped form in which the gripper was brought before applying the vacuum. This physical principle is used to pick parts. The flexible skin of the gripper adjusts to all kinds of shapes, and therefore, can be used to realize 3D bin picking. With the help of a force controlled robot, the gripper can be pushed with a consistent force on flexible positions depending of the filling level of the box. A Kuka LBR iiwa with joint torque sensors in all of its seven axis’ was used to achieve a constant contact pressure. This is the basic criteria to achieve a robust picking process.
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.
TRIZ Innovationstechnologie
(2023)
Phytases are widely used food and feed enzymes to improve phosphate availability and reduce anti-nutritional factors. Despite the benefits, enzyme usage is restricted by the harsh conditions in a gastrointestinal tract (pH 2–6) and feed pelleting conditions at high temperatures (60–90 °C). The commercially available phytase Quantum® Blue has been immobilized as CLEAs using glutardialdehyde and soy protein resulting in a residual activity of 33%. The influence of the precipitating agent, precipitant concentration, cross-linker concentration and cross-linking time, sodium borohydride as well as the proteic feeders gluten, soy protein and bovine serum albumin (BSA) has been optimized. The best conditions were 90% (v/v) ethyl lactate as precipitating reagent, 100 mM glutardialdehyde and a soy protein concentration of 227 mg/L with a cross-linking time of 1 h. The intrinsically stable phytase remained its high thermal stability and temperature optimum. The phytase-CLEA achieved a 425% increase of residual activity under harsh acidic conditions between pH 2.2 and 3.5 compared to the free enzyme. The free and immobilized phytase were deployed in an in vitro assay simulating the acidic conditions in the gizzard of poultry at pH 2. The hydrolysis of phytate was monitored using a novel high-performance thin-layer chromatography (HPTLC) analysis and DAD scanner to study the InsPx fingerprint. All lower inositol phosphate pools (InsP1–InsP6) and free phosphate were separated and analyzed. The phytase-CLEA efficiently released 80% of the total phosphate within 180 min, whereas the free enzyme only released 6% in the same time under the same conditions.
In this work the nonlinear behavior of layered surface acoustic wave (SAW) resonators is studied with the help of finite element (FE) computations. The full calculations depend strongly on the availability of accurate tensor data. While there are accurate material data for linear computations, the complete sets of higher-order material constants, needed for nonlinear simulations, are still not available for relevant materials. To overcome this problem, scaling factors were used for each available nonlinear tensor. The approach here considers piezoelectricity, dielectricity, electrostriction, and elasticity constants up to the fourth order. These factors act as a phenomenological estimate for incomplete tensor data. Since no set of fourth-order material constants for LiTaO3 is available, an isotropic approximation for the fourth-order elastic constants was applied. As a result, it was found that the fourth-order elastic tensor is dominated by one-fourth order Lamé constant. With the help of the FE model, derived in two different, but equivalent ways, we investigate the nonlinear behavior of a SAW resonator with a layered material stack. The focus was set to third-order nonlinearity. Accordingly, the modeling approach is validated using measurements of third-order effects in test resonators. In addition, the acoustic field distribution is analyzed.
Landing heel first has been associated with elevated external knee abduction moments (KAM), thereby potentially increasing the risk of sustaining a non-contact ACL injury. Apart from the foot strike angle, knee valgus angle (VAL) and vertical center of mass velocity at initial ground contact (IC) have been associated with increased KAM in females across different sidestep cuts. While real-time biofeedback training has been proven effective for gait retraining [4], the highly dynamic, non-cyclical nature of cutting maneuvers makes real-time feedback unsuitable and alternative approaches necessary. This study aimed at assessing the efficacy of immediate software-aided feedback on cutting technique in reducing KAM during handball-specific cutting maneuvers.
Due to globalization and the resulting increase in competition on the market, products must be produced more and more cheaply, especially in series production, because buyers expect new variants or even completely new products in ever shorter cycles. Injection molding is the most important production process for manufacturing plastic components in large quantities. However, the conventional production of a mold is extremely time-consuming and costly, which creates a contradiction to the fast pace of the market. Additive tooling is an area of application of additive manufacturing, which in the field of injection molding is preferably used for the prototype production of mold inserts. This allows injection molding tools to be produced faster and more cheaply than through the subtractive manufacturing of metal tools. Material Jetting processes using polymers (MJT-UV/P), also called Polyjet Modeling (PJM), have a great potential for use in additive tooling. Due to the poorer mechanical and thermal properties compared to conventional mold insert materials, e.g. steel or aluminum, the previously used design principles cannot be applied. Accordingly, new design guidelines are necessary, which are developed in this paper. The necessary information is obtained with the help of a systematic literature research. The design guidelines are mapped in a uniform design guide, which is structured according to the design process of injection molds. The guidelines do not only refer to the constructive design of the injection mold or the polymer mold insert, but to the entire design process and describe the four phases of planning, conception, development and realization. Particular attention is paid to the special geometric designs of a polymer mold insert and the thermomechanical properties of the mold insert materials. As a result, design guidelines are available that are adapted to the special requirements of additive tooling of molds inserts made of plastics for injection molding.
Visual programming languages (VPL) let users develop software programs by combining visual program elements, like lists of objects, loops or conditional statements rather than by specifying them textually.
Humanoid robots programming is a very attractive and motivating application domain for students, especially for programming beginners. Humanoid robots are constructed in such a way that they mimic the human body by using actuators that perform like muscles. Typically, a humanoid robot consists of sensors and actuators, i.e. torso, a head, two arms, and two legs, though some humanoid robots may replicate only part of the body, for example, from the waist up. In some cases, humanoid robots are equipped with heads designed to replicate additional human facial features such as eyes. Additional sensors are needed by a robot to gather information about the conditions of the environment to allow the robot to make necessary decisions about its position or certain actions that the situation requires, e.g. an arm movement or an open/close hand action. Other examples for sensor are reflective infrared sensors used to detect objects in proximity.
In this work, we introduce a use-case centered approach based on sensors and actors of a robot and a workflow model to visually describe the sequence of actions including conditional actions or concurrent actions. We provide an in-depth discussion of a new VPL based teaching method for programming humanoid robots based on VPLs. Open research challenges, limits and perspectives for further development of our teaching approach are discussed as well.
The main advantage of mobile context-aware applications is to provide effective and tailored services by considering the environmental context, such as location, time, nearby objects and other data, and adapting their functionality according to the changing situations in the context information without explicit user interaction. The idea behind Location-Based Services (LBS) and Object-Based Services (OBS) is to offer fully-customizable services for user needs according to the location or the objects in a mobile user's vicinity. However, developing mobile context-aware software applications is considered as one of the most challenging application domains due to the built-in sensors as part of a mobile device. Visual Programming Languages (VPL) and hybrid visual programming languages are considered to be innovative approaches to address the inherent complexity of developing programs. The key contribution of our new development approach for location and object-based mobile applications is a use case driven development approach based on use case templates and visual code templates to enable even programming beginners to create context-aware mobile applications. An example of the use of the development approach is presented and open research challenges and perspectives for further development of our approach are formulated.
Sensors and actuators enable creation of context-aware applications in which applications can discover and take advantage of contextual information, such as user location, nearby people and objects. In this work, we use a general context definition, which can be applied to various devices, e.g., robots and mobile devices. Developing context-based software applications is considered as one of the most challenging application domains due to the sensors and actuators as part of a device. We introduce a new development approach for context-based applications by using use-case descriptions and Visual Programming Languages (VPL). The introduction of web-based VPLs, such as Scratch and Snap, has reinvigorated the usefulness of VPLs. We provide an in-depth discussion of our new VPL based method, a step by step development process to enable development of context-based applications. Two case studies illustrate how to apply our approach to different problem domains: Context-based mobile apps and context-based humanoid robot applications.
LogIKTram
(2023)
Der Anstieg des urbanen Verkehrs belastet zunehmend die Anwohner, die Nutzer der Infrastruktur sowie die Umwelt. Während für die Personenbeförderung die Straßenbahnen eine Entlastung bieten, existieren für den innerstädtischen Logistikverkehr keine passenden Angebote. Aus diesem Grund verfolgt das Projekt LogIKTram das Ziel, den Logistikverkehr mit einer Gütertram auf die Schiene zu verlagern. Hierfür werden ein Logistikkonzept sowie verschiedene Planungsmodelle entwickelt, die eine vereinfachte Nutzung erlauben.
Sustainable Production
(2023)
Human interaction frequently includes decision-making processes during which interactants call on verbal and non-verbal resources to manage the flow of interaction. In 2017, Stevanovic et al. carried out pioneering work, analyzing the unfolding of moment-by-moment dynamics by investigating the behavioral matching during search and decision-making phases. By studying the similarities in the participant's body sway during a conversation task in Finnish, the authors showed higher behavioral matching during decision phases than during search phases. The purpose of this research was to investigate the whole-body sway and its coordination during joint search and decision-making phases as a replication of the study by Stevanovic et al. (2017) but based on a German population. Overall, 12 dyads participated in this study and were asked to decide on 8 adjectives, starting with a pre-defined letter, to describe a fictional character. During this joint-decision task (duration: 206.46 ± 116.08 s), body sway of both interactants was measured using a 3D motion capture system and center of mass (COM) accelerations were computed. Matching of body sway was calculated using a windowed cross correlation (WCC) of the COM accelerations. A total of 101 search and 101 decision phases were identified for the 12 dyads. Significant higher COM accelerations (5.4*10−3 vs. 3.7*10−3 mm/s2, p < 0.001) and WCC coefficients (0.47 vs. 0.45, p = 0.043) were found during decision-making phases than during search phases. The results suggest that body sway is one of the resources humans use to communicate the arrival at a joint decision. These findings contribute to a better understanding of interpersonal coordination from a human movement science perspective.
Femtosecond (fs) time-resolved magneto-optics is applied to investigate laser-excited ultrafast dynamics of one-dimensional nickel gratings on fused silica and silicon substrates for a wide range of periodicities Λ = 400–1500 nm. Multiple surface acoustic modes with frequencies up to a few tens of GHz are generated. Nanoscale acoustic wavelengths Λ/n have been identified as nth-spatial harmonics of Rayleigh surface acoustic wave (SAW) and surface skimming longitudinal wave (SSLW), with acoustic frequencies and lifetimes being in agreement with theoretical calculations. Resonant magnetoelastic excitation of the ferromagnetic resonance (FMR) by SAW’s third spatial harmonic, and, most interestingly fingerprints of the parametric resonance at 1/2 SAW frequency have been observed. Numerical solutions of Landau–Lifshitz–Gilbert (LLG) equation magnetoelastically driven by complex polychromatic acoustic fields quantitatively reproduce all resonances at once. Thus, our results provide a solid experimental and theoretical base for a quantitative understanding of ultrafast fs-laser-driven magnetoacoustics and tailoring the magnetic-grating-based metasurfaces at the nanoscale.
In this study, circular economy (CE) relevance in Germany will be discussed based on LinkedIn readily available data. LinkedIn company profiles located in Germany with ‘circular economy’ in their description or any other field were selected and used as a data source to analyze their CE relation. Overall, 514 German companies were analyzed in reference to the 15 German regions they belong. Most companies are located in the federal state of Berlin (126), followed by North Rhine-Westphalia (96) and Bavaria (77). In terms of the industry sector, they are self-classified to environmental services (64), management consulting (50), renewables & environment (33), research (31), and computer software (18) etc. Regarding their employees with LinkedIn profiles, 22,621 people are affiliated with these companies, ranging from one to 7,877. All examined companies have a total of 819,632 followers on LinkedIn, ranging from none to 88,167. An increase in CE-related companies was recorded in 13 of the 16 federal states of Germany over a one-year period. This work provides essential insights into the increasing relevance and trends of the circular economy in German enterprises and will help conduct further national studies with readily available data from LinkedIn.
Die Erfindung betrifft in einem ersten Aspekt eine Vorrichtung zur transkutanen Aufbringung eines elektrischen Stimulationsreizes auf ein Ohr. Die Vorrichtung umfasst einen Schaltungsträger, mindestens zwei Elektroden sowie eine Steuerungseinheit, wobei die Steuerungseinheit dazu konfiguriert ist, anhand von Stimulationsparametern ein elektrisches Stimulationssignal an den Elektroden zu erzeugen. Dabei ist die Vorrichtung, insbesondere eine Oberfläche des Schaltungsträgers der Vorrichtung, auf eine anatomische Form eines Ohres angepasst, sodass Elektroden auf der Oberfläche des Schaltungsträgers aufgebracht sind und ausgewählte Bereiche des Ohres kontaktieren Die Vorrichtung ist dadurch kennzeichnet, dass diese weiterhin einen Sensor zur Erkennung mindestens eines physiologischen Parameter umfasst und eine Steuerungseinheit dazu konfiguriert ist, anhand des mindestens einen physiologischen Parameters die Stimulationsparameter für den Stimulationsreiz anzupassen.In einem weiteren Aspekt betrifft die Erfindung ein Verfahren zur Herstellung der erfindungsgemäßen Vorrichtung.
Public educational institutions are increasingly confronted with a decline in the number of applicants, which is why competition between colleges and universities is also intensifying. For this reason, it is important to position oneself as an institution in order to be perceived by the various target groups and to differentiate oneself from the competition. In this context, the brand and thus its perception and impact play a decisive role, especially in view of the desired communication of the institution's own values and its self-image, the brand identity. To this end, emotions serve as an approach to creating positive stimulation and brand loyalty.
Bei vielen Schulungen, Unterrichten und Weiterbildungen kommen Präsentationen zum Einsatz, um ausbildungsrelevante Inhalte zu vermitteln. Oft sind diese jedoch nicht interessant und zielführend gestaltet, was sich z. B. durch ein Übermaß an Text auszeichnet. Die Autoren stellen alternativ eine visualisierte Aufbereitung von Inhalten vor. Ziel ist es, komplexe Sachverhalte als einfache Bilder und Skizzen komprimiert darzustellen. Mit Hilfe der vorgestellten Methoden können beispielsweise Übungen effizienter vorbereitet, Einsätze übersichtlich erfasst, aber auch alltägliche Situationen vereinfacht kommuniziert werden.
Motivated by the recent trend towards the usage of larger receptive fields for more context-aware neural networks in vision applications, we aim to investigate how large these receptive fields really need to be. To facilitate such study, several challenges need to be addressed, most importantly: (i) We need to provide an effective way for models to learn large filters (potentially as large as the input data) without increasing their memory consumption during training or inference, (ii) the study of filter sizes has to be decoupled from other effects such as the network width or number of learnable parameters, and (iii) the employed convolution operation should be a plug-and-play module that can replace any conventional convolution in a Convolutional Neural Network (CNN) and allow for an efficient implementation in current frameworks. To facilitate such models, we propose to learn not spatial but frequency representations of filter weights as neural implicit functions, such that even infinitely large filters can be parameterized by only a few learnable weights. The resulting neural implicit frequency CNNs are the first models to achieve results on par with the state-of-the-art on large image classification benchmarks while executing convolutions solely in the frequency domain and can be employed within any CNN architecture. They allow us to provide an extensive analysis of the learned receptive fields. Interestingly, our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters practically translate into well-localized and relatively small convolution kernels in the spatial domain.
Polyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected by the selection of input features. In this study, we investigated different combinations of 13 EMG-derived features obtained from EMG signals of healthy individuals performing upper limb movements and tested their performance for movement classification using an Artificial Neural Network. We found that input data (i.e., the set of input features) can be reduced by more than 50% without any loss in accuracy, while diminishing the computing time required to train the classifier. Our results indicate that input features must be properly selected in order to optimize prosthetic control.
State-of-the-art models for pixel-wise prediction tasks such as image restoration, image segmentation, or disparity estimation, involve several stages of data resampling, in which the resolution of feature maps is first reduced to aggregate information and then sequentially increased to generate a high-resolution output. Several previous works have investigated the effect of artifacts that are invoked during downsampling and diverse cures have been proposed that facilitate to improve prediction stability and even robustness for image classification. However, equally relevant, artifacts that arise during upsampling have been less discussed. This is significantly relevant as upsampling and downsampling approaches face fundamentally different challenges. While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling. Blurring is therefore not an option and dedicated operations need to be considered. In this work, we are the first to explore the relevance of context during upsampling by employing convolutional upsampling operations with increasing kernel size while keeping the encoder unchanged. We find that increased kernel sizes can in general improve the prediction stability in tasks such as image restoration or image segmentation, while a block that allows for a combination of small-size kernels for fine details and large-size kernels for artifact removal and increased context yields the best results.
An international study summarizes the threat situation in the OT environment under the heading "Growing security threats" [1]. According to this study, attacks on automation systems are likely to increase in the future. Accordingly, an automation system must be able to protect the integrity of the transmitted information in the future. This requirement is motivated, among other things, by the fact that the network-side isolation of industrial communication systems is no longer considered sufficient as the sole protective measure. This paper uses the example of PROFINET to show how the future requirements for a real-time communication protocol can be met and how they can be derived from the IEC 62443 standard.
There is an ongoing debate about the use and scope of Clayton M. Christensen´s idea of disruptive innovation, including the question of whether it is a management buzz phrase or a valuable theory. This discussion considers the general question of how innovation in the field of management theories and concepts finds its way to the different target groups. This conceptual paper combines the different concepts of the creation and dissemination of management trends in a basic framework based on a short review of models for the dissemination of management ideas. This framework allows an analysis of the character of new management ideas like disruptive innovation. By measuring the impact of the theory on the academic sphere using a bibliometric statistic of the number of academic publications on Google scholar and Scopus and a meta-analysis of research papers, we show the significant influence of disruptive innovation beyond pure management fads.
The main focus of this chapter is the theoretical and instrumental processes that underpin densitometric methods widely used in thin-layer chromatography (TLC). Densitometric methods include UV–vis, luminescence, and fluorescence optical measurements as well as infrared and Raman spectroscopic measurements. The chapter is divided in two general parts: a theoretical part and a practical part. The systems for direct radioactivity measurements and the combination of TLC with mass spectrometry are also discussed. All these systems allow measuring an intensity distribution directly on a TLC plate. We call this “in situ detection” because no analyte is removed from the plate.
The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases.
CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision.
Virtual-Reality
(2023)
Die Virtual-Reality (VR) Technologie ermöglicht Unternehmen eine Produktpräsentation, die weit über traditionelle Darstellungsmethoden hinausgeht. Obgleich die Integration der VR-Technologie für Unternehmen viele Chancen eröffnet, ist deren Einsatz auch mit Risiken verbunden. Insbesondere der Mangel an empirisch gesicherten Erkenntnissen zur Kundenakzeptanz, zu den Auswirkungen der Nutzung sowie zu Kannibalisierungseffekten ist ein wesentlicher Grund, der die Verbreitung von VR in der Kundenkommunikation noch hemmt. Das Buch adressiert diese Forschungslücken und identifiziert mittels eines nutzerzentrierten, quantitativen Forschungsdesigns konkrete Chancen und Risiken, die mit dem Einsatz von VR-Produktpräsentationen verbunden sind.
Das Zeitalter der Digitalisierung ist geprägt durch einen erhöhten Wettbewerb. Eine Chance, bei steigendem Wettbewerb erfolgreich zu bestehen, liegt daher nur in der durchgängigen Digitalisierung von Produktionsunternehmen. Dieser Beitrag stellt eine dreistufige generische Unternehmensmodellplattform Industrie 4.0 vor, die die Durchgängigkeit von Prozessen vom Kunden bis zum Lieferanten auf allen Unternehmensebenen in den Mittelpunkt stellt. Die Schritte zur Bewertung und Gestaltung des Fortschritts auf dem Weg zum digitalisierten Produktionsunternehmen werden aufgezeigt.
Lithium-ion battery cells exhibit a complex and nonlinear coupling of thermal, electrochemical,and mechanical behavior. In order to increase insight into these processes, we report the development of a pseudo-three-dimensional (P3D) thermo-electro-mechanical model of a commercial lithium-ion pouch cell with graphite negative electrode and lithium nickel cobalt aluminum oxide/lithium cobalt oxide blend positive electrode. Nonlinear molar volumes of the active materials as function of lithium stoichiometry are taken from literature and implemented into the open-source software Cantera for convenient coupling to battery simulation codes. The model is parameterized and validated using electrical, thermal and thickness measurements over a wide range of C-rates from 0.05 C to 10 C. The combined experimental and simulated analyses show that thickness change during cycling is dominated by intercalation-induced swelling of graphite, while swelling of the two blend components partially cancel each other. At C-rates above 2 C, electrochemistry-induced temperature increase significantly contributes to cell swelling due to thermal expansion. The thickness changes are nonlinearly distributed over the thickness of the electrode pair due to gradients in the local lithiation, which may accelerate local degradation. Remaining discrepancies between simulation and experiment at high C-rates might be attributed to lithium plating, which is not considered in the model at present.
Electrochemical pressure impedance spectroscopy (EPIS) is an emerging tool for the diagnosis of polymer electrolyte membrane fuel cells (PEMFC). It is based on analyzing the frequency response of the cell voltage with respect to an excitation of the gas-phase pressure. Several experimental studies in the past decade have shown the complexity of EPIS signals, and so far there is no agreement on the interpretation of EPIS features. The present study contributes to shed light into the physicochemical origin of EPIS features, by using a combination of pseudo-two-dimensional modeling and analytical interpretation. Using static simulations, the contributions of cathode equilibrium potential, cathode overpotential, and membrane resistance on the quasi-static EPIS response are quantified. Using model reduction, the EPIS responses of individual dynamic processes are predicted and compared to the response of the full model. We show that the EPIS signal of the PEMFC studied here is dominated by the humidifier. The signal is further analyzed by using transfer functions between various internal cell states and the outlet pressure excitation. We show that the EPIS response of the humidifier is caused by an oscillating oxygen molar fraction due to an oscillating mass flow rate.
A balcony photovoltaic (PV) system, also known as a micro-PV system, is a small PV system consisting of one or two solar modules with an output of 100–600 Wp and a corresponding inverter that uses standard plugs to feed the renewable energy into the house grid. In the present study we demonstrate the integration of a commercial lithium-ion battery into a commercial micro-PV system. We firstly show simulations over one year with one second time resolution which we use to assess the influence of battery and PV size on self-consumption, self-sufficiency and the annual cost savings. We then develop and operate experimental setups using two different architectures for integrating the battery into the micro-PV system. In the passive hybrid architecture, the battery is in parallel electrical connection to the PV module. In the active hybrid architecture, an additional DC-DC converter is used. Both architectures include measures to avoid maximum power point tracking of the battery by the module inverter. Resulting PV/battery/inverter systems with 300 Wp PV and 555 Wh battery were tested in continuous operation over three days under real solar irradiance conditions. Both architectures were able to maintain stable operation and demonstrate the shift of PV energy from the day into the night. System efficiencies were observed comparable to a reference system without battery. This study therefore demonstrates the feasibility of both active and passive coupling architectures.
Given the looming threats of climate change and the rapid worldwide urbanization, it is a necessity to prioritize the transition towards a carbon-free built environment. This research study provides a holistic digital methodology for parametric design of urban residential buildings with regard to the Mediterranean semi-arid climate zone of Morocco in the early design phase. The morphological parameters of the urban residential buildings, namely the buildings’ typology, the distance between buildings, the urban grid’s orientation, and the window-towall ratio, are evaluated in order to identify the key combinations of passive and active solar design strategies that determine the high energy performing configurations, based on the introduced Energy Performance Index (EPI), which is the ratio between solar BIPV production to maximum available installed BIPV capacity and the normalized thermal energy needs. Through an automated processing of 2187 iterations via Grasshopper, we simulate daylight autonomy, indoor thermal comfort and solar rooftop photovoltaic and building integrated photovoltaic (BIPV) energy potential. Then, we analyze the conflicting objectives of energy efficiency measures, active solar design strategies, and indoor visual comfort in the decision-making process that supports our goal of getting closer to net zero urban residential buildings. The digital workflow showed interesting trends in reaching a balanced equilibrium between performance metrics influenced by the contrasting impact of solar exposure on indoor daylight autonomy and thermal energy demand. Furthermore, the study’s findings indicate that it is possible to achieve an annual load match exceeding 66,56 % while simultaneously ensuring an acceptable visual indoor comfort (sDA higher than 0.4). The findings also highlight the important role of the BIPV system in shifting towards the net zero energy goal, by contributing up to 30 % of the overall solar energy output and covering up to 20 % of the yearly self-consumption. Moreover, the energy balance evaluation on an hourly basis indicates that BIPV system notably enhances the daily load cover factor by up to 5.5 %, particularly in the case of slab SN typology, throughout the different seasons. Graphical representations of the yearly, monthly and hourly load matches and the hourly energy balance of the best performing configurations provide a thorough understanding of the potential evolution of the urban energy system over time as a result of the gradual integration of active solar electricity production.
Energy efficiency and hygrothermal performance of hemp clay walls for Moroccan residential buildings
(2023)
Hemp-based building envelopes have gained significant popularity in developed countries, and now the trend of constructing houses with hemp-clay blocks is spreading to developing countries like Morocco. Investigating the hygrothermal behavior of such structures under actual climate conditions is essential for advancing and promoting this sustainable practice. This paper presents an in-depth experimental characterization of a commercial hemp-clay brick that has been exposed to the outdoor environment for four years, in addition to field measurements on a building scale demonstration prototype. Additionally, the study simulates 17 representative cities to assess the hygrothermal performance and energy-saving potential in each of Morocco's six existing climate zones, using the EnergyPlus engine. The experimental campaign's findings demonstrate excellent indoor air temperature and relative humidity regulation within the hemp-clay wall building, leading to satisfactory levels of thermal comfort within hemp-clay wall buildings. This is attributed to the material's good thermal conductivity and excellent moisture buffering capacity (found to be 0.31 W/mK and 2.25 g/m2%RH), respectively). The energy simulation findings also point to significant energy savings, with cooling and heating energy reductions ranging from 27.7% to 47.5% and 33.7% to 79.8%, respectively, as compared to traditional Moroccan buildings.
Subjects utilizing a cochlear implant (CI) in one ear and a hearing aid (HA) on the contralateral ear suffer from mismatches in stimulation timing due to different processing latencies of both devices. This device delay mismatch leads to a temporal mismatch in auditory nerve stimulation. Compensating for this auditory nerve stimulation mismatch by compensating for the device delay mismatch can significantly improve sound source localization accuracy. One CI manufacturer has already implemented the possibility of mismatch compensation in its current fitting software. This study investigated if this fitting parameter can be readily used in clinical settings and determined the effects of familiarization to a compensated device delay mismatch over a period of 3–4 weeks. Sound localization accuracy and speech understanding in noise were measured in eleven bimodal CI/HA users, with and without a compensation of the device delay mismatch. The results showed that sound localization bias improved to 0°, implying that the localization bias towards the CI was eliminated when the device delay mismatch was compensated. The RMS error was improved by 18% with this improvement not reaching statistical significance. The effects were acute and did not further improve after 3 weeks of familiarization. For the speech tests, spatial release from masking did not improve with a compensated mismatch. The results show that this fitting parameter can be readily used by clinicians to improve sound localization ability in bimodal users. Further, our findings suggest that subjects with poor sound localization ability benefit the most from the device delay mismatch compensation.
Lithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed from the Fickian diffusion law, where the transport domain is discretized using finite volumes. Within this physical structure, unknown parameters (diffusion coefficient, diffusion length, discretization) and dependencies (state of charge, lithium concentration) are replaced by neural networks and learnable parameters. We perform model-to-model comparisons, using as training data (a) a Fickian diffusion process, (b) a Warburg element, and (c) a resistor-capacitor circuit. Voltage dynamics during constant-current operation and pulse tests as well as electrochemical impedance spectra are simulated. The slow dynamics of all three physical models in the order of ten to 30 min are well captured by the Grey-box model, demonstrating the flexibility of the present approach.
This paper presents a framework for numerical building validation enhancement based on detailed building specifications from in-situ measurements and evidence-based validation assessment undertaken on a detached sustainable lightweight building in a semi-arid climate. The validation process has been undergone in a set of controlled experiments – a free-float period, and steady-state internal temperatures. The validation was conducted for a complete year with a 1-min time step for the hourly indoor temperature and the variable refrigerant flow (VRF) energy consumption. The initial baseline model was improved by three series of validation steps per three different field measurements including thermal transmittance, glazing thermal and optical properties, and airtightness. Then, the accurate and validated model was used for building energy efficiency assessment in 12 regions of Morocco. This study aims to assess the effect of accurate building characteristics values on the numerical model enhancement. The initial CV(RMSE) and NMBE have improved respectively from 14.58 % and −11.23 %–7.85 % and 1.86 % for the indoor temperature. Besides, from 31.17 % to 14.37 %–20.57 % and 9.77 % for energy consumption. The findings demonstrate that the lightweight construction with the use of a variable refrigerant flow system could be energy efficient in the southern regions of Morocco.
Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.
The variable refrigerant flow system is one of the best heating, ventilation, and air conditioning systems (HVAC) thanks to its ability to provide thermal comfort inside buildings. But, at the same time, these systems are considered one of the most energy-consuming systems in the building sector. Thus, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. Although many researchers have studied the optimization of the building energy performance considering heating or cooling needs, using air handling units, radiant floor heating, and direct expansion valves, few studies have considered the use of multi-objective optimization using only the thermostat setpoints of VRF systems for both cooling and heating needs. Thus, the main aim of this study is to conduct a sensitivity analysis and a multi-objective optimization strategy for a residential building containing a variable refrigerant flow system, to evaluate the effect of the building performance on energy consumption and improve the building energy efficiency. The numerical model was based on the EnergyPlus, jEPlus, and jEPlus+EA simulation engines. The approach used in this paper has allowed us to reach significant quantitative energy saving by varying the cooling and heating setpoints and scheduling scenarios. It should be stressed that this approach could be applied to several HVAC systems to reduce energy-building consumption.
To improve the building’s energy efficiency many parameters should be assessed considering the building envelope, energy loads, occupation, and HVAC systems. Fenestration is among the most important variables impacting residential building indoor temperatures. So, it is crucial to use the most optimal energy-efficient window glazing in buildings to reduce energy consumption and at the same time provide visual daylight comfort and thermal comfort. Many studies have focused on the improvement of building energy efficiency focusing on the building envelope or the heating, ventilation, and cooling systems. But just a few studies have focused on studying the effect of glazing on building energy consumption. Thus, this paper aims to study the influence of different glazing types on the building’s heating and cooling energy consumption. A real case study building located under a semi-arid climate was used. The building energy model has been conducted using the OpenStudio simulation engine. Building indoor temperature was calibrated using ASHRAE’s statistical indices. Then a comparative analysis was conducted using seven different types of windows including single, double, and triple glazing filled with air and argon. Tripleglazed and double-glazed windows with argon space offer 37% and 32% of annual energy savings. It should be stressed that the methodology developed in this paper could be useful for further studies to improve building energy efficiency using optimal window glazing.
Soiling is an important issue in the renewable energy sector since it can result in significant yield losses, especially in regions with higher pollution or dust levels. To mitigate the impact of soiling on photovoltaic (PV) plants, it is essential to regularly monitor and clean the panels, as well as develop accurate soiling predictions that can affect cleaning strategies and enhance the overall performance of PV power plants. This research focuses on the problem of soiling loss in photovoltaic power plants and the potential to improve the accuracy of soiling predictions. The study examines how soiling can affect the efficiency and productivity of the modules and how to measure and predict soiling using machine learning (ML) algorithms. The research includes analyzing real data from large-scale ground-mounted PV sites and comparing different soiling measurement methods. It was observed that there were some deviations in the real soiling loss values compared to the expected values for some projects in southern Spain, thus, the main goal of this work is to develop machine learning models that could predict the soiling more accurately. The developed models have a low mean square error (MSE), indicating the accuracy and suitability of the models to predict the soiling rates. The study also investigates the impact of different cleaning strategies on the performance of PV power plants and provides a powerful application to predict both the soiling and the number of cleaning cycles.
Encapsulant-free N.I.C.E. modules have strong ecological advantages compared to conventional laminated modules but suffer generally from lower electrical performance. Via long-term outdoor monitoring of fullsize industrial modules of both types with identical solar cells, we investigated if the performance difference remains constant over time and which parameters influence its value. After assessing about a full year’s data, two obvious levers for N.I.C.E. optimization are identified: The usage of textured glass and transparent adhesives on the module rear side. Also, the performance loss could be alleviated using tracking systems due to lower AOI values. Our measurements show additionally that N.I.C.E. module surfaces are in average about 2.5°C cooler compared to laminated modules. With these findings, we lay out a roadmap to reduce today’s LIV gap of about 5%rel by different optimizations.
In this paper we report on further success of our work to develop a multi-method energy optimization which works with a digital twin concept. The twin concept serves to replicate production processes of different kinds of production companies, including complex energy systems and test market interactions to then use them for model predictive optimizing. The presented work finally reports about the performed flexibility assessment leading to a flexibility audit with a list of measures and the impact of energy optimizations made related to interactions with the local power grid i.e., the exchange node of the low voltage distribution grid. The analysis and continuous exploration of flexibilities as well as the exchange with energy markets require a “guide” leading to continuous optimization with a further tool like the Flexibility Survey and Control Panel helping decision-making processes on the day-ahead horizon for real production plants or the investment planning to improve machinery, staff schedules and production
infrastructure.
Covid-19 war mit Schulschließungen, Fernunterricht und Kontaktsperren ein bislang unbekannter Einschnitt für Bildungseinrichtungen. Zugleich wurden mit Laptops und Tablets, Web und App flächendeckend digitale Medien im Unterricht etabliert. Wird daraus jetzt das »neue Normal« oder müssen auf Basis aktueller Studien womöglich andere Schlüsse gezogen werden? Dieser Band versammelt Beiträge von Vertretern aus Wissenschaft, Schulpraxis und Kinderheilkunde. So entsteht ein faktenbasiertes und praxistaugliches Fundament für die Frage, was die zentralen Parameter für Schule und gelingenden Unterricht sind.