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This paper presents the first part of a research-work conducted at the University of Applied Sciences (HFT- Stuttgart). The aim of the research was to investigate the potential of low-cost renewable energy systems to reduce the energy demand of the building sector in hot and dry areas. Radiative cooling to the night sky represents a low-cost renewable energy source. The dry desert climate conditions promote radiative cooling applications. The system technology adopted in this work is based on uncovered solar thermal collectors integrated into the building’s hydronic system. By implementing different control strategies, the same system could be used for cooling as well as for heating applications. This paper focuses on identifying the collector parameters which are required as the coefficients to configure such an unglazed collector for calibrating its mathematical model within the simulation environment. The parameter identification process implies testing the collector for its thermal performance. This paper attempts to provide an insight into the dynamic testing of uncovered solar thermal collectors (absorbers), taking into account their prospective operation at nighttime for radiative cooling applications. In this study, the main parameters characterizing the performance of the absorbers for radiative cooling applications are identified and obtained from standardized testing protocol. For this aim, a number of plastic solar absorbers of different designs were tested on the outdoor test-stand facility at HFT-Stuttgart for the characterization of their thermal performance. The testing process was based on the quasi-dynamic test method of the international standard for solar thermal collectors EN ISO 9806. The test database was then used within a mathematical optimization tool (GenOpt) to determine the optimal parameter settings of each absorber under testing. Those performance parameters were significant to compare the thermal performance of the tested absorbers. The coefficients (identified parameters) were used then to plot the thermal efficiency curves of all absorbers, for both the heating and cooling modes of operation. Based on the intended main scope of the system utilization (heating or cooling), the tested absorbers could be benchmarked. Hence, one of those absorbers was selected to be used in the following simulation phase as was planned in the research-project.
During the first years of the last decade, Egypt used to face recurrent electricity cut-offs in summer. In the past few years, the electricity tariff dramatically increased. Radiative cooling to the clear night sky is a renewable energy source that represents a relative solution. The dry desert climate promotes nocturnal radiative cooling applications. This study investigates the potential of nocturnal radiative cooling systems (RCSs) to reduce the energy consumption of the residential building sector in Egypt. The system technology proposed in this work is based on uncovered solar thermal collectors integrated into the building hydronic system. By implementing different control strategies, the same system could be used for both cooling and heating applications. The goal of this paper is to analyze the performance of RCSs in residential buildings in Egypt. The dynamic simulation program TRNSYS was used to simulate the thermal behavior of the system. The relevant issues of Egypt as a case-study are firstly overviewed. Then the paper introduces the work done to develop a building model that represents a typical residential apartment in Egypt. Typical occupancy profiles were developed to define the internal thermal gains. The adopted control strategy to optimize the system operation is presented as well. To fully understand and hence evaluate the operation of the proposed RCS, four simulation cases were considered: 1. a reference case (fully passive), 2. the stand-alone operation of the RCS, 3. ideal heating & cooling operation (fully-active), and 4. the hybrid-operation (when the active cooling system is supported by the proposed RCS). The analysis considered the main three distinct climates in Egypt, represented by the cities of Alexandria, Cairo and Asyut. The hotter and drier weather conditions resulted in a higher cooling potential and larger temperature differences. The simulated cooling power in Asyut was 28.4 W/m² for a 70 m² absorber field. For a smaller field area of 10 m², the cooling power reached 109 W/m² but with humble temperature differences. To meet the rigorous thermal comfort conditions, the proposed sensible RCS cannot fully replace conventional air-conditioning units, especially in humid areas like Alexandria. When working in a hybrid system, a 10% reduction in the active cooling energy demand could be achieved in Asyut to keep the cooling set-point at 24 °C. This percentage reduction was nearly doubled when the thermal comfort set-point was increased by two degrees (26 °C). In a sensitivity analysis, external shading devices as a passive measure as well as the implementation of the Egyptian code for buildings (ECP306/1–2005) were also investigated. The analysis of this study raised other relevant aspects to discuss, e.g. system-sizing, environmental effects, limitations and recommendations.
Context
In a world of high dynamics and uncertainties, it is almost impossible to have a long-term prediction of which products, services, or features will satisfy the needs of the customer. To counter this situation, the conduction of Continuous Improvement or Design Thinking for product discovery are common approaches. A major constraint in conducting product discovery activities is the high effort to discover and validate features and requirements. In addition, companies struggle to integrate product discovery activities into their agile processes and iterations.
Objective
This paper aims at suggests a supportive tool, the “Discovery Effort Worthiness (DEW) Index”, for product owners and agile teams to determine a suitable amount of effort that should be spent on Design Thinking activities. To operationalize DEW, proposals for practitioners are presented that can be used to integrate product discovery into product development and delivery.
Method
A case study was conducted for the development of the DEW index. In addition, we conducted an expert workshop to develop proposals for the integration of product discovery activities into the product development and delivery process.
Results
First, we present the "Discovery Effort Worthiness Index" in form of a formula. Second, we identified requirements that must be fulfilled for systematic integration of product discovery activities into product development and delivery. Third, we derived from the requirements proposals for the integration of product discovery activities with a company's product development and delivery.
Conclusion
The developed "Discovery Effort Worthiness Index" provides a tool for companies and their product owners to determine how much effort they should spend on Design Thinking methods to discover and validate requirements. Integrating product discovery with product development and delivery should ensure that the results of product discovery are incorporated into product development. This aims to systematically analyze product risks to increase the chance of product success.
The development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training.
Sleep is an essential part of human existence, as we are in this state for approximately a third of our lives. Sleep disorders are common conditions that can affect many aspects of life. Sleep disorders are diagnosed in special laboratories with a polysomnography system, a costly procedure requiring much effort for the patient. Several systems have been proposed to address this situation, including performing the examination and analysis at the patient's home, using sensors to detect physiological signals automatically analysed by algorithms. This work aims to evaluate the use of a contactless respiratory recording system based on an accelerometer sensor in sleep apnea detection. For this purpose, an installation mounted under the bed mattress records the oscillations caused by the chest movements during the breathing process. The presented processing algorithm performs filtering of the obtained signals and determines the apnea events presence. The performance of the developed system and algorithm of apnea event detection (average values of accuracy, specificity and sensitivity are 94.6%, 95.3%, and 93.7% respectively) confirms the suitability of the proposed method and system for further ambulatory and in-home use.
Healthy sleep is one of the prerequisites for a good human body and brain condition, including general well-being. Unfortunately, there are several sleep disorders that can negatively affect this. One of the most common is sleep apnoea, in which breathing is impaired. Studies have shown that this disorder often remains undiagnosed. To avoid this, developing a system that can be widely used in a home environment to detect apnoea and monitor the changes once therapy has been initiated is essential. The conceptualisation of such a system is the main aim of this research. After a thorough analysis of the available literature and state of the art in this area of knowledge, a concept of the system was created, which includes the following main components: data acquisition (including two parts), storage of the data, apnoea detection algorithm, user and device management, data visualisation. The modules are interchangeable, and interfaces have been defined for data transfer, most of which operate using the MQTT protocol. System diagrams and detailed component descriptions, including signal requirements and visualisation mockups, have also been developed. The system's design includes the necessary concepts for the implementation and can be realised in a prototype in the next phase.
The influence of sleep on human health is enormous. Accordingly, sleep disorders can have a negative impact on it. To avoid this, they should be identified and treated in time. For this purpose, objective (with an appropriate device) or subjective (based on perceived values) measurement methods are used for sleep analysis to understand the problem. The aim of this work is to find out whether an exchange of the two methods is possible and can provide reliable results. In accordance with this goal, a study was conducted with people aged over 65 years old (a total of 154 night-time recordings) in which both measurement methods were compared. Sleep questionnaires and electronic devices for sleep assessment placed under the mattress were applied to achieve the study aims. The obtained results indicated that the correlation between both measurement methods could be observed for sleep characteristics such as total sleep time, total time in bed and sleep efficiency. However, there are also significant differences in absolute values of the two measurement approaches for some subjects/nights, which leads us to conclude that the substitution is more likely to be considered in case of long-term monitoring where the trends are of more importance and not the absolute values for individual nights.
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.
Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone's home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.
Adoption of artificial intelligence (AI) has risen sharply in recent years but many firms are not successful in realising the expected benefits or even terminate projects before completion. While there are a number of previous studies that highlight challenges in AI projects, critical factors that lead to project failure are mostly unknown. The aim of this study is therefore to identify distinct factors that are critical for failure of AI projects. To address this, interviews with experts in the field of AI from different industries are conducted and the results are analyzed using qualitative analysis methods. The results show that both, organizational and technological issues can cause project failure. Our study contributes to knowledge by reviewing previously identified challenges in terms of their criticality for project failure based on new empirical data, as well as, by identifying previously unknown factors.