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- Institut für Strategische Innovation und Technologiemanagement - IST (29)
- Institut für Systemdynamik - ISD (64)
- Institut für Werkstoffsystemtechnik Konstanz - WIK (5)
We propose and apply a requirements engineering approach that focuses on security and privacy properties and takes into account various stakeholder interests. The proposed methodology facilitates the integration of security and privacy by design into the requirements engineering process. Thus, specific, detailed security and privacy requirements can be implemented from the very beginning of a software project. The method is applied to an exemplary application scenario in the logistics industry. The approach includes the application of threat and risk rating methodologies, a technique to derive technical requirements from legal texts, as well as a matching process to avoid duplication and accumulate all essential requirements.
The IT unit is not the only provider of information technology (IT) used in business processes. Aiming for increased performance, many business workgroups autonomously implement IT resources not covered by their organizational IT service management. This is called shadow IT. Risks and inefficiencies associated with this phenomenon challenge organizations. Organizations need to decide how to deal with identified shadow IT and if the business or the IT unit should be responsible for corresponding tasks and components. This study proposes design principles for a method to control identified shadow IT following action design research in four organizational settings. The procedure results in an allocation of IT task responsibilities between the business workgroups and the IT unit following risk considerations and transaction cost economics, leading to an IT service governance. This contributes to governance research regarding adaptive and efficient arrangements with reduced risks for business-located IT activities.
Konventionelle, von Dieselmotoren angetriebene Radlader beeinträchtigen die Lebensqualität der Menschen in ihrer unmittelbaren Umgebung mit Lärm- und Schadstoffemissionen. Das vom BMBF geförderte Forschungsvorhaben "Emissionsarmer Elektroradlader" verfolgt das Ziel, die lokalen Emissionen von Radladern deutlich herabzusetzen und die Effizienz des Fahrzeugs zu steigern. Im Rahmen des Vorhabens wurde ein konventioneller Radlader auf elektrische Antriebe umgerüstet. Als Energiespeicher dient eine LiFeYPO4-Batterie, die für eine Betriebsdauer von vier Stunden ausgelegt ist. In ersten praktischen Untersuchungen wurde die Energiebilanz des Emissionsarmen Elektro-Radladers mit der des konventionellen Serienfahrzeugs verglichen. Dazu wurde ein modifizierter Y-Arbeitszyklus entworfen, der sich an den üblichen Arbeitsaufgaben des Radladers orientiert und sich durch eine hohe Reproduzierbarkeit auszeichnet. Für die vollständige Bewertung wird die komplette Kette der Energieumwandlung betrachtet, beginnend mit der Energie im Kraftstoff bzw. der dem Stromnetz entnommenen Energie, bis zur mechanischen Arbeit, die das Gerät verrichtet. Daraus lassen sich Rückschlüsse auf die unterschiedlichen CO2-Emissionen beider Fahrzeuge ableiten.
InBetween
(2017)
This PhD investigation lies at the intersection of Architecture, Textile Design and Interaction Design and speculates about sustainable forms of future living, focussing on bionic principles to create alternative lightweight building structures with textiles and digital fabrication techniques. In an interdisciplinary, practice- based design approach, informed by radical case studies from the 1960s to 80s on soft architectures like Archigram, Buckminster Fuller, Cedric Price, or Yona Friedman and critical theory on new materialism, (D. Haraway 1997, K. Barad 1998, J. Bennett 2007) sociological, philosophical (B. Latour 2005, G.Deleuze F., Guattari F 1987) and phenomenological thinkers (L. Malafouris 2005, J.Rancière 2004, B. Massumi 2002, N. Bourriaud 2002 , M. Merleau-Ponty 1963) this research investigates the cultural and social rootedness (Verortung) of novel materials and technologies, exploring in between prosthetic relations between the body and the environment.
We consider the problem of increasing the informative value of electrocardiographic (ECG) surveys using data from multichannel electrocardiographic leads, that include both recorded electrocardiosignals and the coordinates of the electrodes placed on the surface of the human torso. In this area, we were interested in reconstruction of the surface distribution of the equivalent sources during the cardiac cycle at relatively low hardware cost. In our work, we propose to reconstruct the equivalent electrical sources by numerical methods, based on integral connection between the density of electrical sources and potential in a conductive medium. We consider maps of distributions of equivalent electric sources on the heart surface (HSSM), presenting source distributions in the form of a simple or double electrical layer. We indicate the dynamics of the heart electrical activity by the space-time mapping of equivalent electrical sources in HSSM.
In this paper, utilisation of an Unscented Kalman Filter for concurrently performing disturbance estimation and wave filtering is investigated. Experimental results are provided that demonstrate very good performance subject to both tasks. For the filter, a dynamic model has been used which was optimised via correlation analysis in order to obtain a minimum set of relevant parameters. This model has also been validated by experiments deploying a small vessel. A simulation study is presented to evaluate the performance using known quantities. Experimental trials have been performed on the Rhine river. The results show that for instance flow direction and varying current velocities can continuously be estimated with decent precision, even while the boat is performing turning manoeuvres. Moreover, the filtering properties are very satisfactory. This makes the filter suitable for being used, for instance, in autonomous vessel applications or assistance systems.
In this paper, a systematic comparison of three different advanced control strategies for automated docking of a vessel is presented. The controllers are automatically tuned offline by applying an optimization process using simulations of the whole system including trajectory planner and state and disturbance observer. Then investigations are conducted subject to performance and robustness using Monte Carlos simulation with varying model parameters and disturbances. The control strategies have also been tested in full scale experiments using the solar research vessel Solgenia. The investigated control strategies all have demonstrated very good performance in both, simulation and real world experiments. Videos are available under https://www.htwg-konstanz.de/forschung-und-transfer/institute-und-labore/isd/regelungstechnik/videos/
Durch den Einsatz von mobilen Endgeräten (z.B. Tablets, Smartphones) erschließen sich immer mehr Möglichkeiten, die Ausführung von Geschäftsprozessen zu unterstützen. Beispielsweise können Geschäftsprozessaktivitäten (z.B. Genehmigung eines Angebots) ortsunabhängig bearbeitet werden, wodurch die Durchlaufzeit signifikant reduziert wird. Die Nutzung von mobilen Apps beschränkt sich hierbei meist nur auf die Unterstützung von effizienter und flexibler Interaktion zwischen den verschiedenen ausführenden Rollen. Dieser Artikel beschreibt, wie mobile Apps nicht nur die Ausführung, sondern auch die Optimierung von Geschäftsprozessen unterstützen können. Hierzu werden vordefinierte Qualitätskriterien kontextabhängig während der Ausführung von Aktivitäten erfasst. Die durch traditionelle Methoden erfassten Daten (z.B. Messung von Kennzahlen) werden somit durch in Echtzeit gesammeltes User Feedback ergänzt. Der Ansatz wird am Beispiel einer eigens entwickelten mobilen App demonstriert und evaluiert.
Online-based business models, such as shopping platforms, have added new possibilities for consumers over the last two decades. Aside from basic differences to other distribution channels, customer reviews on such platforms have become a powerful tool, which bestows an additional source for gaining transparency to consumers. Related research has, for the most part, been labelled under the term electronic word-of-mouth (eWOM). An approach, providing a theoretical basis for this phenomenon, will be provided here. The approach is mainly based on work in the field of consumer culture theory (CCT) and on the concept of co-creation. The work of several authors in these streams of research is used to construct a culturally informed resource-based theory, as advocated by Arnould & Thompson and Algesheimer & Gurâu.
The evolution of strain induced martensite in austenitic stainless steel AISI 304 was investigated in a rolling contact on a two-discs-tribometer. The effects of surface roughness, slip and normal force as well as the number of load cycles were examined. In comparison to the investigations of martensitic phase transformation during cold rolling, the applied stresses are considerably lower. The formation of strain induced martensite was detected in-situ by means of a FERITSCOPE MP30 and ex-situ by optical microscopy after etching with Kane etchant. Both number of load cycles and magnitude of normal force appeared to be the main influencing factors regarding strain induced martensitic evolution in low stress rolling contacts.
Engineering and management
(2019)
Low temperature carburizing of a series of austenitic stainless with various combinations of chromium and nickel equivalents was performed. The investigation of the response towards low temperature carburized for three stainless steels with various Cr- and Ni-equivalents showed that the carbon uptake depends significantly on the chemical composition of the base material. The higher carbon content in the expanded austenite layer of specimen 6 (1.4565) and specimen 4 (1.4539/AISI 904L) compared to specimen 2 (1.4404/AISI 316L) is assumed to be mainly related to the difference in the specimens’ chromium content. More chromium leads to more lattice expansion. Along with the higher carbon content, higher hardness values and higher compressive residual stresses in the expanded austenite zone are introduced than for low temperature carburized AISI 316L. The residual stresses obtained from X-ray diffraction lattice strain investigation depend strongly on the chosen X-ray elastic constants. Presently, no values are known for carbon (or nitrogen) stabilized expanded austenite. Nevertheless, first principle elastic constants for γ′&minus Fe4C appear to provide realistic residual stress values. Magnetic force microscopy and measurement with an eddy current probe indicate that austenitic stainless steels can become ferromagnetic upon carburizing, similar for low temperature nitriding. The apparent transition from para- to ferromagnetism cannot be attributed entirely to the interstitially dissolved carbon content in the formed expanded austenite layer but appears to depend also on the metallic composition of the alloy, in particular the Ni content.
In biomechanics laboratories the ground reaction force time histories of the foot-fall of persons are usually measured using a force plate. The accelerations of the floor, in which the force plate is embedded, have to be limited, as they may influence the accuracy of the force measurements. For the numerical simulation of vibrations induced by humans in biomechanical laboratories, loading scenarios are defined. They include continuous motions of persons (walking, running) as well as jumps, typical for biomechanical investigations on athletes. The modeling of floors has to take into account the influence of floor screed in case of portable force plates. Criteria for the assessment of the measuring error provoked by floor vibrations are given. As an example a floor designed to accommodate a force platform in a biomechanical laboratory of the University Hospital in Tübingen, Germany, has been investi-gated for footfall induced vibrations. The numerical simulation by a finite element analysis has been validated by field measurements. As a result, the measuring error of the force plate installed in the laboratory is obtained for diverse scenarios.
Comparison and Identifiability Analysis of Friction Models for the Dither Motion of a Solenoid
(2018)
In this paper, the mechanical subsystem of a proportional solenoid excited by a dither signal is considered. The objective is to find a suitable friction model that reflects the characteristic mechanical properties of the dynamic system. Several different friction models from the literature are compared. The friction models are evaluated with respect to their accuracy as well as their practical identifiability, the latter being quantified based on the Fisher information matrix.
An approach for an adaptive position-dependent friction estimation for linear electromagnetic actuators with altered characteristics is proposed in this paper. The objective is to obtain a friction model that can be used to describe different stages of aging of magnetic actuators. It is compared to a classical Stribeck friction model by means of model fit, sensitivity, and parameter correlation. The identifiability of the parameters in the friction model is of special interest since the model is supposed to be used for diagnostic and prognostic purposes. A method based on the Fisher information matrix is employed to analyze the quality of the model structure and the parameter estimates.
We present an innovative decision support system (DSS) for distribution system operators (DSO) based on an artificial neural network (ANN). A trained ANN has the ability to recognize problem patterns and to propose solutions that can be implemented directly in real time grid management. The principle functionality of this ANN based optimizer has been demonstrated by means of a simple virtual electrical grid. For this grid, the trained ANN predicted the solution minimizing the total line power dissipation in 98 percent of the cases considered. In 99 percent of the cases, a valid solution in compliance with the specified operating conditions was found. First ANN tests on a more realistic grid, calibrated with household load measurements, revealed a prediction rate between 88 and 90 percent depending on the optimization criteria. This approach promises a faster, more cost-efficient and potentially secure method to support distribution system operators in grid management.
In many industrial applications a workpiece is continuously fed through a heating zone in order to reach a desired temperature to obtain specific material properties. Many examples of such distributed parameter systems exist in heavy industry and also in furniture production such processes can be found. In this paper, a real-time capable model for a heating process with application to industrial furniture production is modeled. As the model is intended to be used in a Model Predictive Control (MPC) application, the main focus is to achieve minimum computational runtime while maintaining a sufficient amount of accuracy. Thus, the governing Partial Differential Equation (PDE) is discretized using finite differences on a grid, specifically tailored to this application. The grid is optimized to yield acceptable accuracy with a minimum number of grid nodes such that a relatively low order model is obtained. Subsequently, an explicit Runge-Kutta ODE (Ordinary Differential Equation) solver of fourth order is compared to the Crank-Nicolson integration scheme presented in Weiss et al. (2022) in terms of runtime and accuracy. Finally, the unknown thermal parameters of the process are estimated using real-world measurement data that was obtained from an experimental setup. The final model yields acceptable accuracy while at the same time shows promising computation time, which enables its use in an MPC controller.
This paper describes the development of a control system for an industrial heating application. In this process a moving substrate is passing through a heating zone with variable speed. Heat is applied by hot air to the substrate with the air flow rate being the manipulated variable. The aim is to control the substrate’s temperature at a specific location after passing the heating zone. First, a model is derived for a point attached to the moving substrate. This is modified to reflect the temperature of the moving substrate at the specified location. In order to regulate the temperature a nonlinear model predictive control approach is applied using an implicit Euler scheme to integrate the model and an augmented gradient based optimization approach. The performance of the controller has been validated both by simulations and experiments on the physical plant. The respective results are presented in this paper.
This paper presents a modeling approach of an industrial heating process where a stripe-shaped workpiece is heated up to a specific temperature by applying hot air through a nozzle. The workpiece is moving through the heating zone and is considered to be of infinite length. The speed of the substrate is varying over time. The derived model is supposed to be computationally cheap to enable its use in a model-based control setting. We start by formulating the governing PDE and the corresponding boundary conditions. The PDE is then discretized on a spatial grid using finite differences and two different integration schemes, explicit and implicit, are derived. The two models are evaluated in terms of computational effort and accuracy. It turns out that the implicit approach is favorable for the regarded process. We optimize the grid of the model to achieve a low number of grid nodes while maintaining a sufficient amount of accuracy. Finally, the thermodynamical parameters are optimized in order to fit the model's output to real-world data that was obtained by experiments.
Analysing observability is an important step in the
process of designing state feedback controllers. While for linear
systems observability has been widely studied and easy-to-check
necessary and sufficient conditions are available, for nonlinear
systems, such a general recipe does not exist and different classes
of systems require different techniques. In this paper, we analyse
observability for an industrial heating process where a stripe-
shaped plastic workpiece is moving through a heating zone where
it is heated up to a specific temperature by applying hot air to its
surface through a nozzle. A modeling approach for this process
is briefly presented, yielding a nonlinear Ordinary Differential
Equation model. Sensitivity-based observability analysis is used
to identify unobservable states and make suggestions for addi-
tional sensor locations. In practice, however, it is not possible
to place additional sensors, so the available measurements are
used to implement a simple open-loop state estimator with
offset compensation and numerical and experimental results are
presented.
This paper describes an early lumping approach for generating a mathematical model of the heating process of a moving dual-layer substrate. The heat is supplied by convection and nonlinearly distributed over the whole considered spatial extend of the substrate. Using CFD simulations as a reference, two different modelling approaches have been investigated in order to achieve the most suitable model type. It is shown that due to the possibility of using the transition matrix for time discretization, an equivalent circuit model achieves superior results when compared to the Crank-Nicolson method. In order to maintain a constant sampling time for the in-visioned-control strategies, the effect of variable speed is transformed into a system description, where the state vector has constant length but a variable number of non-zero entries. The handling of the variable transport speed during the heating process is considered as the main contribution of this work. The result is a model, suitable for being used in future control strategies.
Misbehave like Nobody’s Watching? Investor Attention to Corporate Misconduct and its Implications
(2023)
There have been substantial research efforts for algorithms to improve continuous and automated assessment of various health-related questions in recent years. This paper addresses the deployment gap between those improving algorithms and their usability in care and mobile health applications. In practice, most algorithms require significant and founded technical knowledge to be deployed at home or support healthcare professionals. Therefore, the digital participation of persons in need of health care professionals lacks a usable interface to use the current technological advances. In this paper, we propose applying algorithms taken from research as web-based microservices following the common approach of a RESTful service to bridge the gap and make algorithms accessible to caregivers and patients without technical knowledge and extended hardware capabilities. We address implementation details, interpretation and realization of guidelines, and privacy concerns using our self-implemented example. Also, we address further usability guidelines and our approach to those.
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.
A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal’s length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for ap- plication to raw data and a complex multi-class multi-label classification problem, even if the training data is from di- verse datasets and of differing lengths.
The reliable supply of energy is an essential prerequisite for the economic success of a country. Questions of sustainability and the replacement of import dependencies require new tasks with new approaches. This contribution provides an overview of dependencies using the example of German electrical power grids integrating renewable energies. Aspects of energy trading and grid stability are brought into connection, stock exchange trading, grid codes and volatility of used primary energies are discussed.
Nowadays there is a rich diversity of sleep monitoring systems available on the market. They promise to offer information about sleep quality of the user by recording a limited number of vital signals, mainly heart rate and body movement. Typically, fitness trackers, smart watches, smart shirts, smartphone applications or patches do not provide access to the raw sensor data. Moreover, the sleep classification algorithm and the agreement ratio with the gold standard, polysomnography (PSG) are not disclosed. Some commercial systems record and store the data on the wearable device, but the user needs to transfer and import it into specialised software applications or return it to the doctor, for clinical evaluation of the data set. Thus an immediate feedback mechanism or the possibility of remote control and supervision are lacking. Furthermore, many such systems only distinguish between sleep and wake states, or between wake, light sleep and deep sleep. It is not always clear how these stages are mapped to the four known sleep stages: REM, NREM1, NREM2, NREM3-4. [1] The goal of this research is to find a reduced complexity method to process a minimum number of bio vital signals, while providing accurate sleep classification results. The model we propose offers remote control and real time supervision capabilities, by using Internet of Things (IoT) technology. This paper focuses on the data processing method and the sleep classification logic. The body sensor network representing our data acquisition system will be described in a separate publication. Our solution showed promising results and a good potential to overcome the limitations of existing products. Further improvements will be made and subjects with different age and health conditions will be tested.
Development of an expert system to overpass citizens technological barriers on smart home and living
(2023)
Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the ”Finde-Dein-SmartHome” tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.
Innovation Labs
(2021)
Today's increasing pace of change and intense competition places demands on organizations to use a different approach to innovation, going beyond the incremental innovation that is typically developed within the core of the organization. As an option to escape the existing beliefs of the core organization, innovation labs are used to develop more discontinuous innovation. Despite the abundance of these so-called innovation labs in practice, researchers have devoted little effort to scrutinizing the concept and to provide managers with a framework for exploiting this form of innovation. In this paper, we aim to perform an empirical investigation and to create a consensus around the concept of innovation labs. To do so, we conducted a multiple case study in large international organizations with a total of 31 interviews of an average length of 70 minutes. We offer a framework by identifying four innovation lab types and consider when each is most appropriate. Furthermore, we highlight the importance for managers and their organizations to align the strategic intent with the innovation lab type as well as the interface between the innovation lab and the core business.
Regional economies clearly benefit from thriving entrepreneurial ecosystems. However, ecosystems are not yet entirely gender-inclusive and therefore are not tapping their full potential. This is most critical with respect to technology-based entrepreneurship which features the largest gender imbalance. Despite the considerably growing amount of literature in the two research fields of female entrepreneurship and entrepreneurial ecosystems, the intersection of the two areas has not yet been outlined. We depict the state of knowledge with a structured review of the literature highlighting bibliometric information, methods used, and the main topics addressed in current articles. From there, recommendations for future research are derived.
This paper broadens the resource-based approach to explaining survival of new technology-based firms (NTBFs) by focusing on the entrepreneur's ability to transform resources in response to triggers resulting from market interactions. Network theory is used to define a construct that allows determining the status of venture emergence (VE).The operationalization of the VE construct is built on the firm's value network maturity in the four market dimensions customer, investor, partner, and human resource. Business plans of NTBFs represent the artifact that contains this data in the form of transaction relation descriptions. Using content analysis, a multi-step combined human and computer coding process has been developed to empirically determine NTBFs' status of VE.Results of the business plan analysis suggests that the level of transaction relations allows to draw conclusions on the status of VE. Moreover, applying the developed process, a business plan coding test shows that the transaction relation based VE status significantly relates to NTBFs' survival capabilities.
We examine to what extent a transaction relation-based value network maturity status of New Technology-Based Firms (NTBFs) is related to their survival. A specific challenge of NTBFs is their lack of market-orientation, which is why the maturity of the ties they form towards the market in terms of customers, financiers, personnel and partners is supposed to be a strong indicator for survival. We analyze a sample of 170 NTBFs by capturing their value network status from business plans and defining their survival status using secondary research. Simple statistical tests and regressions suggest that the official registration of the business is a pre-step for survival that requires industry-specific value network dimension strengths. A sub-sample survival analysis shows that for all NTBFs that have reached registration, regardless of their industry, a stronger customer value network maturity dimension prevents from failure and is thus a significant predictor for survival. Moreover, the analyses partly support the idea that NTBFs from the IT sector are less dependent on a strong value network in the financier dimension to survive. The results are of relevance for both practitioners and researchers in the innovation system: a better understanding of the factors impacting on NTBF survival can help to provide more tailored support services for young firms, increase the effectiveness of resource allocations, and provide a basis for further research.
Text produced by entrepreneurs represents a data source in entrepreneurship research on venture performance and fund-raising success. Manual text coding of single variables is increasingly assisted or replaced by computer-aided text analysis. Yet, for the development of prediction models with several variables, such dictionary-based text analysis methods are less suitable. Natural language processing techniques are an alternative; however, the implementation is more complex and requires substantial programming skills. More work is required to understand how text analytics can advance entrepreneurship research. This study hence experiments with different artificial intelligence methods rooted in Natural Language Processing and deep learning. It uses 766 business plans to train a model for the automated measurement of transaction relations, a construct which is an indicator for new technology-based firm survival. Empirical findings show that the accuracy of construct measurement can be significantly increased with automated methods and improves with larger amounts of training data. Language complexity sets limits to the precision of automated construct measurement though. We therefore recommend a hybrid approach: making use of the inherent advantages of combining automated with human coding until the amount of training data is sufficiently large to substitute the human coding completely. The study provides insights into the applicability of different text analytics methods in entrepreneurship research and points at future research potential.
Business coaching is believed to effectively improve survival and success chances of new technology-based firms (NTBFs). However, not much empirical evidence on the support measure's effectiveness is available. Therefore, a pragmatic two-armed Randomized Controlled Trial (RCT) to test the effect of tactical business coaching on NTBF survival capabilities was designed and, for the most part, carried out. However, due to a lower than expected sample size and great attrition between groups, the RCT reveals deviations from the trial design that impede a thorough data assessment. Based on the data given, a first data analysis does not reveal significant differences in survival capability between the two groups. Thus, to provide guidance for future RCTs in business contexts, lessons learned about how to deal with trickle samples and experiment constellations with third parties carrying out the intervention are drawn.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
The expansion of a given multivariate polynomial into Bernstein polynomials is considered. Matrix methods for the calculation of the Bernstein expansion of the product of two polynomials and of the Bernstein expansion of a polynomial from the expansion of one of its partial derivatives are provided which allow also a symbolic computation.