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Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario.
Erst die Corona-Pandemie, dann der Krieg in der Ukraine und die Energiekrise. Es scheint als rutschten wir von einer Katastrophe in die andere ohne zu wissen was als Nächstes kommt. Wir müssen uns der Frage stellen, wie wir solchen Krisen zukünftig begegnen können.
Auch die Forscherinnen und Forscher an der Hochschule Reutlingen leisten einen Beitrag dazu, unsere Gesellschaft widerstandsfähiger und robuster zu machen – sei es durch pfiffige Lösungen für die Energiekrise, durch kompetente Beratung zu Ressourceneffizienz und Lieferketten oder durch aktuelle Forschungsansätze zu resilienten IT-Strukturen und einer resilienten Wirtschaft.
From the perspective of manufacturing companies, the political, media and economic discourse on decarbonisation in the recent years manifests itself as an increasing social expectation of action. In Germany, in particular, this discourse is also being driven forward by powerful companies, respectively sectors, most notably the automotive industry. Against this background, the present paper examines how German manufacturing companies react to rising societal pressure and emerging policies. It examines which measures the companies have taken or plan to take to reduce their carbon footprint, which aspirations are associated with this and the structural characteristics (company size, energy intensity, and sector) by which these are influenced. A mix methods approach is applied, utilising data gathered from approx. 900 companies in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on the announced decarbonisation plans and initiatives. We demonstrate that one-size-serves-all approaches are not suitable to decarbonise industry, as the situation and ambitions differ considerably depending on size, energy intensity and sector. Even though the levels of ambition and urgency are high, micro and energy intensive companies, in particular, are challenged. The present research uncovers a series of questions that call for attention to materialise the ambitions and address the challenges outlined.
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
The blockchain technology represents a decentralized database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain manufacturing processes to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. However, the decentralized and immutable structure of blockchain technology also creates challenges when applying these token-based approaches to dynamic manufacturing processes. As a first step, this paper investigates existing mapping approaches and exemplifies weaknesses regarding their suitability for products with changeable configurations. Secondly, a concept is proposed to overcome these weaknesses by introducing logically coupled tokens embedded into a flexible smart contract structure. Finally, a concept for a token-based architecture is introduced to map manufacturing processes of products with changeable configurations.
This study empirically analyzes and compares return data from developed and emerging market data based on the Fama French five-factor model and compares it to previous results from the Fama French three-factor model by Kostin, Runge and Adams (2021). It researches whether the addition of the profitability and investment pattern factors show superior results in the assessment of emerging markets during the COVID-19 pandemic compared to developed markets. We use panel data covering eight indices of developed and emerging countries as well as a selection of eight companies from these markets, covering a period from 2000 to 2020. Our findings suggest that emerging markets do not generally outperform developed markets. The results underscore the need to reconsider the assumption that adding more factors to regression models automatically yields results that are more reliable. Our study contributes to the extant literature by broadening this research area. It is the first study to compare the performance of the Fama French three-factor model and the Fama French five-factor model in the cost of equity calculation for developed and emerging countries during the COVID-19 pandemic and other crisis events of the past two decades.
Evaluation of human-robot order picking systems considering the evolution of object detection
(2022)
The automation of intralogistic processes is a major trend, but order picking, one of the core and most cost-intensive tasks in this field, remains mostly manual due to the flexibility required during picking. Reacting to its hard physical and ergonomic strain, the automation of this process is however highly relevant. Robotic picking system would enable the automation of this process from a technical point of view, but the necessity for the system to evolve in time, due to dynamics of logistic environments, faces operations with new challenges that are hardly treated in literature. This unknown scares potential investors, hindering the application of technically feasible solutions. In this paper, a model for the evaluation of the additional cost of training of automated systems during operations is presented, that also considers the savings enabled by the system after its evolution. The proposed approach, that considers different parameters such as capacity, ergonomics and cost, is validated with a case study and discussed.
According to several surveys and statistics, the great majority of companies previously not accustomed to automation are piloting solutions to automate business processes. Those accustomed to automation also attempt to introduce more of it, focusing on automation-unfriendly processes that remained manual. However, when the decision on what and whether to automate is not trivial for evident reasons, even industry leaders may get stuck on an overwhelming question: where to begin automating? The question remains too often unanswered as state-of-the-art methods fail to consider the whole picture. This paper introduces a holistic approach to the decision-making for investments in automation. The method supports the iterative analysis and evaluation of operative processes, providing tools for a quantitative approach to the decision-making. Thanks to the method, a large pool of processes can be first considered and then filtered out in order to select the one that yields the best value for the automation in the specific context. After introducing the method, a case study is reported for validation before the discussion.
Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers.
Hintergrund: Endoskopische Operationsverfahren haben sich als Goldstandard in der Nasennebenhöhlen-(NNH-)Chirurgie etabliert. Den sich daraus ergebenden Herausforderungen für die chirurgische Ausbildung kann durch den Einsatz von Virtuelle-Realität-(VR-)Trainingssimulatoren begegnet werden. Bislang wurde eine Reihe von Simulatoren für NNH-Operationen entwickelt. Frühere Studien im Hinblick auf den Trainingseffekt wurden jedoch nur mit medizinisch vorgebildeten Probanden durchgeführt oder es wurde nicht über dessen zeitlichen Verlauf berichtet.
Methoden: Ein NNH-CT-Datensatz wurde nach der Segmentierung in ein 3-dimensionales, polygonales Oberflächenmodell überführt und mithilfe von originalem Fotomaterial texturiert. Die Interaktion mit der virtuellen Umgebung erfolgte über ein haptisches Eingabegerät. Während der Simulation wurden die Parameter Eingriffsdauer und Fehleranzahl erfasst. Zehn Probanden absolvierten jeweils eine Trainingseinheit bestehend aus je 5 Übungsdurchläufen an 10 aufeinanderfolgenden Tagen.
Ergebnisse: Vier Probanden verringerten die benötigte Zeit um mehr als 60% im Verlauf des Übungszeitraums. Vier der Probanden verringerten ihre Fehleranzahl um mehr als 60%. Acht von 10 Probanden zeigten eine Verbesserung bezüglich beider Parameter. Im Median wurde im gesamten gemessenen Zeitraum die Dauer des Eingriffs um 46 Sekunden und die Fehleranzahl um 191 reduziert. Die Überprüfung eines Zusammenhangs zwischen den 2 Parametern ergab eine positive Korrelation.
Schlussfolgerung: Zusammenfassend lässt sich feststellen, dass das Training am NNH-Simulator auch bei unerfahrenen Personen die Performance beträchtlich verbessert, sowohl in Bezug auf die Dauer als auch auf die Genauigkeit des Eingriffs.
In our initial DaMoN paper, we set out the goal to revisit the results of “Starring into the Abyss [...] of Concurrency Control with [1000] Cores” (Yu in Proc. VLDB Endow 8: 209-220, 2014). Against their assumption, today we do not see single-socket CPUs with 1000 cores. Instead, multi-socket hardware is prevalent today and in fact offers over 1000 cores. Hence, we evaluated concurrency control (CC) schemes on a real (Intel-based) multi-socket platform. To our surprise, we made interesting findings opposing results of the original analysis that we discussed in our initial DaMoN paper. In this paper, we further broaden our analysis, detailing the effect of hardware and workload characteristics via additional real hardware platforms (IBM Power8 and 9) and the full TPC-C transaction mix. Among others, we identified clear connections between the performance of the CC schemes and hardware characteristics, especially concerning NUMA and CPU cache. Overall, we conclude that no CC scheme can efficiently make use of large multi-socket hardware in a robust manner and suggest several directions on how CC schemes and overall OLTP DBMS should evolve in future.
Current data-intensive systems suffer from scalability as they transfer massive amounts of data to the host DBMS to process it there. Novel near-data processing (NDP) DBMS architectures and smart storage can provably reduce the impact of raw data movement. However, transferring the result-set of an NDP operation may increase the data movement, and thus, the performance overhead. In this paper, we introduce a set of in-situ NDP result-set management techniques, such as spilling, materialization, and reuse. Our evaluation indicates a performance improvement of 1.13 × to 400 ×.
Purpose
Returnable transport packaging (RTP) solutions have found increasing attention in the recent past. It is not clear, however, under what conditions an RTP system improves a company's financial performance. This paper investigates the operational factors that influence the financial attractiveness of an RTP solution in a manufacturing environment and discusses how these factors are related to each other.
Design/methodology/approach
The paper presents the results of five empirical RTP use cases and compares the case study findings with the results found in literature in order to develop a taxonomy of RTP cost effects. Drawing on the concept of value-based management (VBM), the operational drivers of these RTP cost effects are systematized and categorized in a value driver model that relates RTP cost effects to overall economic value added (EVA).
Findings
Based on the use case findings, additional cost factors are identified that have not been previously discussed in literature. The amended taxonomy of influence factors is further operationalized in a value driver model.
Originality/value
The present paper is the first one providing a taxonomy of RTP cost effects and putting these effects in a conceptual framework that can be used for decision-making and performance benchmarking.
Der Career Booster
(2022)
Das berufsbegleitende International MBA Program der ESB Business School in Reutlingen hat einen sehr guten Ruf. Der Schwerpunkt liegt neben Strategie und Business Development auf Digitalisierung, Marketing, Leadership und Nachhaltigkeit. Es wird immer wieder den Anforderungen der Wirtschaft angepasst, meint Studiendekan Prof. Gerd Nufer.
Körpersprachen lügen nicht
(2022)
Jeder kennt diese Aussagen: „Ton macht die Musik“, „Die kalte Schulter zeigen“ oder „Von oben herab“. Doch die Frage, was das für unseren täglichen Umgang bedeutet und wie wir dies nutzen können, stellen wir uns nicht. Dies gilt für das private, tägliche Leben, aber auch im Beruf. Im Privaten wundern wir uns, wenn wir die Wahrheit sagen und danach eine Freundschaft beendet ist. Vielleicht war es der Ton der Aussage oder die Haltung zum Gegenüber?
Führungskräfte in der Wirtschaft und in öffentlichen Institutionen sollen Mitarbeiter, Kollegen, Bürger und Kunden „führen“. Die Liste der Adressaten ist nahezu unendlich. Doch wie kann gute Führung gelingen? Das einzige zur Verfügung stehende Mittel, um Menschen zu führen ist die Kommunikation. Klassischerweise konzentrieren sich Manager und Führungskräfte auf die zu vermittelnden Inhalte und Sachinformationen. Dies ist insofern korrekt als es eines guten Inhalts bedarf, um sinnvoll arbeiten zu können. Ein schlechtes Auto oder ein miserabler Service werden durch gute Worte nicht besser.
Fakt ist aber, dass nur ca. 25 bis 30 Prozent einer Kommunikation über die Vermittlung von Sachinformationen vermittelt werden. Wem kann ich vertrauen? Wie ehrlich meint sie es? Was denkt der andere von mir? Sagt sie jetzt die Wahrheit? Alles was wir nicht sagen wollen, das verrät unser Körper. Man kann diese Sprache lesen und verstehen lernen.
Der Ingenieur, Verhaltensforscher und Schauspieler Hans-Jürgen Jensen hat sich in jahrelanger Forschung mit Gesten, Haltung und Mimik befasst. In Theorie und Praxis hat er analysiert, erprobt und genau beobachtet, was hinter dem menschlichen Verhalten und seinen Körperausdrücken steckt. Körpersprache ist ehrlich, deshalb kann jeder sein Gegenüber durchschauen, Gefühle entschlüsseln und Lügen entlarven.
This article illustrates a method for sensorless control of a switched reluctance motor. The detection of the time instants for switching between the working phases is determined based on the evaluation of the switching frequency of the hysteresis current controllers for appropriately selected sensing phases. This enables a simple and cost efficient implementation. The method is compared with a pulse injection method in terms of efficiency and resolution.
Context-aware systems to support actors in the operating room depending on the status of the intervention require knowledge about the current situation in the intra-operative area. In literature, solutions to achieve situation awareness already exist for specific use cases, but applicability and transferability to other conditions are less addressed. It is assumed that a unified solution that can be adapted to different processes and sensors would allow for greater flexibility, applicability, and thus transferability to different applications. To enable a flexible and intervention-independent system, this work proposes a concept for an adaptable situation recognition system. The system consists of four layers with several modular components for different functionalities. The feasibility is demonstrated via prototypical implementation and functional evaluation of a first basic framework prototype. Further development goal is the stepwise extension of the prototype.
Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.
The implementation of human resource (HR) policies often proves troublesome due to the appearance, and stubborn persistence, of gaps in the process. Human resource management (HRM) scholars problematise these gaps and advocate tight implementation to reduce gaps and to ensure the desired impact of policies on organisational performance. Drawing on organisational institutionalism, we contend that gaps in implementing HR policies can actually be productive, as they secure organisational legitimacy, and thus enable organisations to operate viably within several institutional environments. We suggest that different approaches to implementation are needed, some of them premised on accepting sustained implementation gaps. We introduce minimum and moderate implementation approaches, rooted in the notion of decoupling, to complement approaches aimed at tight implementation. Our aim is to support the further development of research based on a richer interpretation of HRM implementation challenges and choices they present for HR managers.
Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause–effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is, therefore, fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation, and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics and address the challenges of limited measurement data and wide parameter ranges from the literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.
Einige Ideen, Erfahrungen und Realitäten für die Studierenden und Bürger in Reutlingen. Zusammengestellt von 50 Studierenden 2020/21 und aus Beiträgen von 40 Institutionen und Unternehmen in und um Reutlingen.
Ein Versuch, sehr konkret am Tatsächlichen zu erklären, was zu mehr Nachhaltigkeit führt, in Reutlingen. Dabei bleibt nicht aus, auch auf Schwachstellen hinzuweisen.
Wenn Studierende und Bürger in den nächsten Jahren bewusst zu mehr Nachhaltigkeit bereit sind, so sind sie mit den Ideen und Realitäten in diesem Projekt auf einem guten Weg.
In dem Beitrag wurden exemplarisch Möglichkeiten aufgezeigt, die mittels der Verknüpfung unterschiedlicher Technologien zur Steigerung von Genauigkeit und Effizienz bei der Bearbeitung genutzt werden können. Dabei sind Kenntnisse aus unterschiedlichen Bereichen erforderlich. Dies sind sowohl Bearbeitungs- und Prozesstechnologie, die Konstruktion von Maschinen, Vorrichtungen und Werkzeugen, sowie Mess- und Steuerungstechnik. Daneben sind auch neue Geschäftsmodelle und Technologien für die Nutzung und Verfügbarmachung von Daten und Informationen erforderlich.
Die additive Fertigung hat sich in den vergangenen Jahren wesentlich weiterentwickelt. Dabei wurde die Prozesstechnologie, Anlagen und die Werkstoffe optimiert. Für die industrielle Anwendung auch bei größeren Stückzahlen in der flexiblen Fertigung fehlen noch automatisierte Lösungen für die gesamte Prozesskette. In diesem Beitrag werden Werkzeuge und Technologie für die Reinigung interner Strukturelemente dargestellt.
Background
Although teledermatology has been proven internationally to be an effective and safe addition to the care of patients in primary care, there are few pilot projects implementing teledermatology in routine outpatient care in Germany. The aim of this cluster randomized controlled trial was to evaluate whether referrals to dermatologists are reduced by implementing a store-and-forward teleconsultation system in general practitioner practices.
Methods
Eight counties were cluster randomized to the intervention and control conditions. During the 1-year intervention period between July 2018 and June 2019, 46 general practitioner practices in the 4 intervention counties implemented a store-and-forward teledermatology system with Patient Data Management System interoperability. It allowed practice teams to initiate teleconsultations for patients with dermatologic complaints. In the four control counties, treatment as usual was performed. As primary outcome, number of referrals was calculated from routine health care data. Poisson regression was used to compare referral rates between the intervention practices and 342 control practices.
Results
The primary analysis revealed no significant difference in referral rates (relative risk = 1.02; 95% confidence interval = 0.911–1.141; p = .74). Secondary analyses accounting for sociodemographic and practice characteristics but omitting county pairing resulted in significant differences of referral rates between intervention practices and control practices. Matched county pair, general practitioner age, patient age, and patient sex distribution in the practices were significantly related to referral rates.
Conclusions
While a store-and-forward teleconsultation system was successfully implemented in the German primary health care setting, the intervention's effect was superimposed by regional factors. Such regional factors should be considered in future teledermatology research.
Die bedarfsgerechte Steuerung dezentraler thermischer Energiesysteme, wie Kraft-Wärme-Kopplungs- (KWK-) Anlagen und Wärmepumpen, kann einen entscheidenden Beitrag zur Deckung bzw. Reduktion der Residuallast leisten und so für eine Verringerung der konventionellen Reststromversorgung und den damit einhergehenden Treibhausgasemissionen sorgen. Dafür wurde an der Hochschule Reutlingen in mehrjähriger Forschungsarbeit ein prognosebasierter Steuerungsalgorithmus entwickelt. Gegenstand dieses Beitrags bilden neben der Vorstellung eben jenes Steuerungsalgorithmus auch dessen praktische Umsetzungsvarianten: Eine auf einer speicherprogrammierbaren Steuerung (SPS) rein lokal ausführbare Version sowie eine Webservice-Anwendung für den parallelen Betrieb mehrerer Anlagen – ausgehend von einem zentralen Server. Erprobungen am KWK-Prüfstand der Hochschule Reutlingen bestätigen die zuverlässige Funktionsweise des Algorithmus in den verschiedenen Umsetzungsvarianten. Gleichzeitig wird der Vorteil der bedarfsgerechten Steuerung gegenüber dem, insbesondere im Mikro-KWK-Bereich standardmäßig vorliegenden, wärmegeführten Betrieb in Form einer Steigerung der Eigenstromdeckung von bis zu 27 % aufgezeigt. Neben der bedarfsgerechten Steuerung bedient der entwickelte Algorithmus zudem noch ein weiteres Anwendungsgebiet: Den vorhersagbaren KWK-Betrieb, der beispielsweise in Form täglicher Einspeiseprognose im Rahmen des Redispatch 2.0 eingefordert wird. Die Vorhersage des KWK-Betriebs ist dabei auf zwei Weisen möglich: Als erste Option kann der wärmegeführte Betrieb direkt über den Algorithmus abgebildet und prognostiziert werden. Eine andere Möglichkeit stellt wiederum die bedarfsgerechte Steuerung der Anlage dar; der berechnete optimale Fahrplan entspricht dabei gleichzeitig der Betriebsprognose des KWK-Geräts. Damit ist der entwickelte Steuerungsalgorithmus in der Lage, auf unterschiedliche Weisen zum Gelingen der Energiewende beizutragen.
Die Zielsetzung des hier vorgestellten Projekts ist es, eine intelligente Steuerungsalgorithmik für Biogas-Blockheizkraftwerke (Biogas-BHKW) zu entwickeln und zu optimieren. Daran schließt sich eine Testphase an einer realen Biogasanlage an, an der die Algorithmik zu diesem Zweck in die Anlagensteuerung implementiert wird. Um beurteilen zu können inwieweit die Steuerungsalgorithmik einen Beitrag zur Entlastung von Stromnetzen leisten kann, wird für die Versuche neben dem elektrischen Bedarf des landwirtschaftlichen Betriebs, an dem die Anlage angesiedelt ist, zusätzlich die Residuallast des benachbarten Stromnetzes betrachtet. Diese basiert auf Daten vom nächstgelegenen Umspannwerk, die so skaliert werden, dass sie eine Siedlung repräsentieren, die von dem Biogas-BHKW der Anlage mitversorgt werden kann. Die Einbindung der Steuerungsalgorithmik in die Anlagensteuerung erfolgt über eine Kommunikationsstruktur mit einer Datenbank als zentraler Schnittstelle. Eine erste Versuchsreihe, bei der das Biogas-BHKW nach den Fahrplänen der intelligenten Steuerungsalgorithmik geregelt wird, zeigt vielversprechende Ergebnisse. Über die gesamte Versuchsreihe hinweg berechnet die Steuerungsalgorithmik zuverlässig neue Fahrpläne, die vom BHKW weitestgehend auch sehr gut umgesetzt werden. Zudem kann nachgewiesen werden, dass durch den Einsatz der Algorithmik das vorgelagerte Stromnetz entlastet wird.
The Principles for Responsible Investments (PRI) is “the world’s leading proponent of responsible investment” (PRI 2021a). With the development of six Principles for Responsible Investment, the PRI supports its international network of investor signatories in incorporating the environmental, social, and governance (ESG) factors into their investment and ownership decisions. The goal of PRI is to develop a more sustainable global financial system by encouraging “investors to use responsible investment to enhance returns and better manage risks” (PRI 2021a). This independent financial initiative is supported by the United Nations and linked to the United Nations Environmental Program Finance Initiative (UNEP FI 2021) and the United Nations Global Compact (UN Global Compact 2021).
Values Management System
(2022)
The ValuesManagementSystem (VWS) is a management standard to “provide a sustainable safeguard of a firm and its development, in all dimensions (legal, economic, ecological, social)” (VWSZfW, p. 4). It includes a framework for values-driven governance through self-commitment and self-binding mechanisms. Values promote a sense of identity and give organizations guidance in decision-making. This is especially important in decision-making processes where topics are not clearly ruled by laws and regulations.
VMSZfW must be embedded in the specific business strategy, structure, and culture of an organization. The following four steps describe the implementation of the ValuesManagementSystemZfW: (i) Codify core values of an organization, for instance, with a “mission, vision and values statement” or Code of Ethics, (ii) implement guidelines such as Code of Conduct and specific policies and procedures, (iii) systematize these by establishing management systems such as Compliance and CSR management systems, and (iv) finally organize and establish structures to ensure the strategic direction and operational implementation and review of these processes. The top management shows that values management is taken seriously by their self-commitment to the core values of the company.
Die Charakterisierung und Beschreibung der komplexen Wechselwirkungen an der Zerspanstelle eines Bearbeitungszentrums beeinflusst die Qualität der hergestellten Bauteile. In diesem Beitrag wird die Messung und Beschreibung der Eigenfrequenzen unterschiedlicher Bearbeitungszentren in Abhängigkeit der bei der Bearbeitung verwendeten Werkzeuge und Bearbeitungsstrategien bezüglich der Auswirkungen auf die Stabilität hergeleitet. Dazu werden die gestellseitigen Resonanzfrequenzen analysiert. Ziel der Untersuchungen ist eine Beschreibung der dynamischen Eigenschaften zur Optimierung der NC-Programmierung.
Sägen ist ein häufig unterschätzter spanender Prozess. Oft kommt das Sägen nur zum Zuschnitt von Rohteilen zum Einsatz. Bei der Bearbeitung von Leichtbauwerkstoffen werden damit jedoch Schichten in Toleranz direkt auf die montagefertigen Konturen zugeschnitten. Zur Steigerung von Qualität und Zuverlässigkeit des Fertigungsprozesses werden maschineninterne und sensorische Daten überwacht, ausgewertet und in den Prozess zurückgekoppelt. Daher kommt es auf die gezielte Kontrolle der entscheidenden Parameter mit möglichst wenigen und robusten Schnittstellen an. Im Rahmen eines ZIM-Kooperationsprojektes (Hochschule Reutlingen, Hema Frickenhausen, Pragmatic Minds Kirchheim) wurde dies für einen Bandsägeprozess erforscht und umgesetzt.
Die additive Fertigung hat sich in den vergangenen Jahren wesentlich weiter entwickelt. Dabei wurde die Prozesstechnologie, Anlagen und die Werkstoffe optimiert. Für die industrielle Anwendung auch bei größeren Stückzahlen in der flexiblen Fertigung fehlen noch automatisierte Lösungen für die gesamte Prozesskette. In diesem Beitrag werden Werkzeuge und Technologie für die Reinigung interner Strukturelemente dargestellt.
This article explores the question of how sustainability and labour law are interrelated. The modern world of work is characterised by the growing social and environmental responsibility of companies. Especially in the post-COVID era, sustainability also plays an increasingly important role in the corporate context, which is also noticeable in the so-called ‘war for talent’. Achieving personal career goals is no longer enough for employees today. Corporate values and in particular the so-called ESG criteria (Environment, Social, Governance) are thus also becoming increasingly important in the employment relationship and in corporate reporting requirements. In terms of social sustainability, labour law instruments can, for example, promote the creation of a discrimination-free working environment, the introduction of flexible working time models or the protection of whistleblowers. From an ecological perspective, labour regulations are also suitable for implementing ‘green mobility’ and other measures to reduce companies’ ecological footprints. Working from home, which experienced a huge boom during the COVID-19 pandemic, is also sustainable, especially from an ecological point of view. Appropriate consideration of these sustainable work tools in future corporate social responsibility (CSR) strategies not only creates a competitive advantage but can also be beneficial in recruitment.
The functionality of existing cyber-physical production systems generally focuses on mapping technologic specifications derived from production requirements. Consequently, such systems base their conception on a structurally mechanistic paradigm. Insofar as these approaches have considered humans, their conception likewise is based on the structurally identical paradigm. Due to the fundamental reorientation towards explicitly human-centered approaches, the fact that essential aspects of the dimension "human" remain unconsidered by the previous paradigm becomes more and more apparent. To overcome such limitations, mapping the "social" dimension requires a structurally different approach. In this paper, an anthropocentric approach is developed based on possible conceptions of the human being, enabling a structural integration of the human being in an extended dimension. Through the model, extending concepts for better integration of the human being in the sense of human-centered approaches, as envisioned in the Industrie 5.0 conception, is possible.
Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.
Physicians in interventional radiology are exposed to high physical stress. To avoid negative long-term effects resulting from unergonomic working conditions, we demonstrated the feasibility of a system that gives feedback about unergonomic
situations arising during the intervention based on the Azure Kinect camera. The overall feasibility of the approach could be shown.
Up to now biorefinery concepts can hardly compete with the conventional production of fossil-based chemicals. On one hand, conventional chemical production has been optimised over many decades in terms of energy, yield and costs. Biorefineries, on the other hand, do not have the benefit of long-term experience and therefore have a huge potential for optimisation. This study deals with the economic evaluation of a newly developed biorefinery concept based on superheated steam (SHS) torrefaction of biomass residues with recovery of valuable platform chemicals. Two variants of the biorefinery were economically investigated. One variant supplies various platform chemicals and torrefied biomass. The second variant supplies thermal energy for external consumers in addition to platform chemicals. The results show that both variants can be operated profitably if the focus of the platform chemicals produced is on high quality and thus on the higher-priced segment. The economic analysis gives clear indications of the most important financial influencing parameters. The economic impact of integration into existing industrial structures is positive. With the analysis, a viable business model can be developed. Based on the results of the present study, an open-innovation platform is recommended for the further development and commercialisation of the novel biorefinery.
The food system represents a key industry for Europe and Germany in particular. However, it is also the single most significant contributor to climate and environmental change. A food system transformation is necessary to overcome the system’s major and constantly increasing challenges in the upcoming decades. One possible facilitator for this transformation are radical and disruptive innovations that start-ups develop. There are many challenges for start-ups in general and food start-ups in particular. Various support opportunities and resources are crucial to ensure the success of food start-ups. One aim of this study is to identify how the success of start-ups in the food system can be supported and further strengthened by actors in the innovation ecosystem in Germany. There is still room for improvement and collaboration toward a thriving innovation ecosystem. A successful innovation ecosystem is characterised by a well-organised, collaborative, and supportive environment with a vivid exchange between the members in the ecosystem. The interviewees confirmed this, and although the different actors are already cooperating, there is still room for improvement. The most common recommendation for improving cooperation is learning from other countries and bringing the best to Germany.
In contrast to classical advertising, event marketing is a dynamic communication instrument that is constantly bringing trends and innovations. The diverse application possibilities and potentials of event marketing make it possible to reach relevant target groups according to the current zeitgeist, to generate brand-relevant realities and worlds of experience, to generate emotions and sympathy values and in this way to create a bond between brand or company and recipients. Enduring brand experience worlds can be seen as a consistent further development of event marketing. Unlike typical branding events, which are limited in time, enduring brand experience worlds create theme worlds that can be experienced, usually for an unlimited period of time. The research paper reflects the development and current state of brand experience worlds. On this basis a systematisation of enduring brand experience worlds is presented and discussed.
Similarities and differences of the various types of enduring brand experience worlds are elaborated and critically appraised.
We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
Hybrid project management is an approach that combines traditional and agile project management techniques. The goal is to benefit from the strengths of each approach, and, at the same time avoid the weaknesses. However, due to the variety of hybrid methodologies that have been presented in the meantime, it is not easy to understand the differences or similarities of the methodologies, as well as, the advantages or disadvantages of the hybrid approach in general. Additionally, there is only fragmented knowledge about prerequisites and success factors for successfully implementing hybrid project management in organizations. Hence, the aim of this study is to provide a structured overview of the current state of research regarding the topic. To address this aim, we have conducted a systematic literature review focusing on a set of specific research questions. As a result, four different hybrid methodologies are discussed, as well as, the definition, benefits, challenges, suitability and prerequisites of hybrid project management. Our study contributes to knowledge by synthesizing and structuring prior work in this growing area of research, which serves as a basis for purposeful and targeted research in the future.
Purpose
Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice.
Methods
In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN.
Conclusion
Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI.
A new planar compact antenna composed of two crossed Cornu spirals is presented. Each Cornu spiral is fed from the center of the linearly part of the curvature between the two spirals, which builds the clothoid. Sequential rotation is applied using a sequential phase network to obtain circular polarization and increase the effective bandwidth. Signal integrity issues have been addressed and designed to ensure high quality of signal propagation. As a result, the antenna shows good radiation characteristics in the bandwidth of interest. Compared to antennas of the same size in the literature, it is broadband and of high gain. Although the proposed antenna has been designed for K- and Ka-band operations, it can also be developed for lower and upper frequencies because of the linearity of the Maxwell equations.
One of the key challenges for automatic assistance is the support of actors in the operating room depending on the status of the procedure. Therefore, context information collected in the operating room is used to gain knowledge about the current situation. In literature, solutions already exist for specific use cases, but it is doubtful to what extent these approaches can be transferred to other conditions. We conducted a comprehensive literature research on existing situation recognition systems for the intraoperative area, covering 274 articles and 95 cross-references published between 2010 and 2019. We contrasted and compared 58 identified approaches based on defined aspects such as used sensor data or application area. In addition, we discussed applicability and transferability. Most of the papers focus on video data for recognizing situations within laparoscopic and cataract surgeries. Not all of the approaches can be used online for real-time recognition. Using different methods, good results with recognition accuracies above 90% could be achieved. Overall, transferability is less addressed. The applicability of approaches to other circumstances seems to be possible to a limited extent. Future research should place a stronger focus on adaptability. The literature review shows differences within existing approaches for situation recognition and outlines research trends. Applicability and transferability to other conditions are less addressed in current work.
Purpose: Interpretive research in management accounting and control provides rich insights from empirically based studies, but it has been criticised for lacking generalisability and potential subjectivity. On the latter, triangulation is useful, and this paper aims to offer some insights on a triangulation technique thus far not commonly reported in management accounting/control research.
Design/methodology/approach: Drawing on a study of the roles of management accountants in performance management systems, this paper offers some insights from empirical experiences on the use of concept maps as a tool to assist triangulation and improve understanding of complex empirical phenomena.
Findings: The concept maps as utilised revealed additional insights which were not recounted by interviewees during the normal interview time. This is a potentially important finding for consideration of future researchers.
Practical implications: In this paper, how concept maps were used is detailed, and it is hoped that future researchers will find their use beneficial in interview settings.
Originality/value: Thus far, concept maps seem under-utilised in management accounting and control research. This paper gives some initial insights on how they may be used in case study settings.
Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained.
Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified.
Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks.
Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
This article explores the determinants of people’s growth prospects in survey data as well as the impact of the European recovery fund to future growth. The focus is on the aftermath of the Corona pandemic, which is a natural limit to the sample size. We use Eurobarometer survey data and macroeconomic variables, such as GDP, unemployment, public deficit, inflation, bond yields, and fiscal spending data. We estimate a variety of panel regression models and develop a new simulation-regression methodology due to limitation of the sample size. We find the major determinant of people’s growth prospect is domestic GDP per capita, while European fiscal aid does not significantly matter. In addition, we exhibit with the simulation-regression method novel scientific insights, significant outcomes, and a policy conclusion alike.
Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27 iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS. These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room.
Purpose
The purpose of this study is to examine private households’ preferences for service bundles in the German energy market.
Design/methodology/approach
This investigation is based on survey data collected from 3,663 customers of seven mainly municipal energy suppliers in the German energy market. The data set was analyzed via a binary logistic regression model to identify the most prospective customers and their preferences regarding bundles of energy services.
Findings
The results indicate that potential adopters of energy-related service bundles have greater prior knowledge about service bundles; place higher importance on simplified handling, flat rates and long price guarantees; prefer to purchase a service bundle from an energy supplier; live in urban areas and have a gas tariff; are both less likely to have a green electricity tariff and to support the German energy transition; have a greater intention to purchase a smart home product; are less likely to already be prosumers; and prefer customer centers and social media as communication channels with energy providers.
Practical implications
This paper offers several implications for decision-makers in developing marketing strategies for bundled offerings in a highly competitive energy market.
Originality/value
This paper contributes to the sparse research on service bundles in the energy sector, despite the growing interest of energy suppliers and consumers in this topic. It expands the research focusing on the telecommunications sector.
Modern production systems are characterized by the increasingly use of CPS and IoT networks. However, processing the available information for adaptation and reconfiguration often occurs in relatively large time cycles. It thus does not take advantage of the optimization potential available in the short term. In this paper, a concept is presented that, considering the process information of the individual heterogeneous system elements, detects optimization potentials and performs or proposes adaptation or reconfiguration. The concept is evaluated utilizing a case study in a learning factory. The resulting system thus enables better exploitation of the potentials of the CPPS.
