Search Results (10)

The rapid urbanization in Southeast Asia has resulted in an increase in vertical apartment buildings, bringing notable changes in residents’ lifestyles accompanied by Westernized cultures. Focusing on Indonesia, this study delves into how residents adapt their behaviors, especially traditional floor-sitting behavior, to living in the different types of apartments. The study also explores energy consumption and thermal comfort in relation to floor-usage behaviors. We conducted a comprehensive questionnaire survey of more than 3300 respondents in Indonesia, with 1841 Jabodetabek samples used for analysis. The findings indicate that approximately 80% of lower-income apartment residents (Rusunawa) predominantly engaged in floor-sitting behavior and relied on fans for cooling. In contrast, approximately 75% of higher-income apartment residents (condominiums) preferred chair-sitting and used air conditioning (AC). Cluster analysis of three key factors—primary posture, foot covering, and floor covering—revealed four distinct groups. The clusters with a lower preference for floor-sitting exhibited approximately 50% higher annual electricity consumption due to AC usage, whereas the clusters favoring floor-sitting consumed less electricity, relying more on fans. However, despite variations in energy use, over 85% of respondents across all clusters were mostly reported as comfortable, indicating that behavioral adaptations with floor-sitting remain viable in achieving thermal comfort. While an increase in income level changes behaviors and energy use, the results suggest that floor-sitting is a traditional practice that serves as an effective low-energy strategy in hot and humid climates. Full article

This study aimed to investigate the effects of prolonged inhalation of nature-derived odors indoors on humans. Twenty-six women participated in this study. Heart rate variability, heart rate, blood pressure, pulse rate, estradiol, testosterone, and cortisol were used as indicators of autonomic nervous system and endocrine system activities. Profile of mood state, state–trait anxiety inventory, menopause rating scale and general sleep disturbance scale were used as psychological indicators. The order was as follows: After the participants relaxed in a chair for 5 min, their heart rate variability and heart rate were measured for 3 min with their eyes closed. Subsequently, blood pressure and pulse rate were measured, salivary samples were collected for estradiol, testosterone, and cortisol analyses, and a subjective assessment was conducted. The participants received a diffuser containing fir essential oil and were instructed on its usage and precautions. Subsequently, they returned home and inhaled the fir oil for a week. After 7 days, participants revisited the laboratory for posttest measurements, conducted at the same time as the pretest. Prolonged inhalation of the fir essential oil resulted in increased estradiol concentration, decreased systolic and diastolic blood pressure, relief of menopausal symptoms, reduced anxiety levels, improved sleep quality and mood states. Prolonged inhalation of the fir essential oil induced physiological and psychological relaxation on menopausal women. Full article

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid. Full article

In the sCO2-HeRo project, the Chair of Turbomachinery at the University of Duisburg-Essen developed, built and tested a turbomachine with an integral design in which the compressor, generator and turbine are housed in a single hermetic casing. However, ball bearings limited operation because their lubricants were incompatible with supercritical CO₂ (sCO₂) and they had to operate in gaseous CO₂ instead. To overcome this problem, the turbomachine was redesigned built and tested in the sCO2-4-NPP project. Instead of ball bearings, magnetic bearings are now used to operate the turbomachine with the entire rotor in sCO₂. This paper presents the revised design, focusing on the usage of magnetic bearings. It also investigates whether the sCO₂ limits the operating range. Test runs show that increasing the density and rotational speed results in greater deflection of the rotor and greater forces on the bearings. Measurements are also analyzed with respect to influence of the density increase on the destabilizing forces in the rotor–stator cavities. The conclusion is that for the operation of the turbomachine, the control parameters of the magnetic bearings must be adjusted not only to the rotor speed, but also to the fluid density. This enabled successful operation of the turbomachine, which reached a speed of about 40,000 rpm during initial tests in CO₂. Full article

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%. Full article

In this paper, an arc was drawn over ten years of research activities from three chairs of the Montanuniversitaet Leoben, as well as industrial partners. The superior objective of this research effort was to develop a wet-mechanical process for the recovery of polyolefin concentrates (90 wt% polyolefins) from mixed waste fraction for use in chemical recycling and to advance this new technology to commercial maturity. As a bridge technology, it would close the gap between state-of-the-art dry processing of mixed plastic waste materials and chemical plastic recycling via thermo-chemical conversion. The methods used were mainly tested in a lab-scale plant with a throughput capacity of 50 to 200 kg/h depending on the bulk density of the used feedstock. Further studies for the treatment and usage of the main products and by-products, as well as chemical analyses of them, were completed during the investigation. Within these series of tests, polyolefin concentrates, which satisfied the requirements for chemical recycling, could be recovered. With these data, a concept for an industrial pilot plant was developed and evaluated from an economic point of view. According to this evaluation, the realization of such an industrial pilot plant can be recommended. Full article

Physical activity and low-level laser therapy (LLLT) can reduce knee osteoarthritis (KOA) inflammation. We are conducting a randomized placebo-controlled trial to investigate the long-term effectiveness of LLLT combined with strength training (ST) in persons with KOA, since it, to our knowledge, has not been investigated before. Fifty participants were enrolled. LLLT and ST was performed 3 times per week over 3 and 8 weeks, respectively. In the LLLT group, 3 Joules of 904 nm wavelength laser was applied to 15 spots per knee (45 Joules/knee/session). The primary outcomes are pain during movement, at night and at rest (Visual Analogue Scale) and global pain (Knee injury and Osteoarthritis Outcome Score, KOOS) pain subscale. The secondary outcomes are KOOS disability and quality-of-life, analgesic usage, global health change, knee active range of motion, 30 s chair stand, maximum painless isometric knee extension strength, knee pain pressure threshold and real-time ultrasonography-assessed suprapatellar effusion, meniscal neovascularization and femur cartilage thickness. All the outcomes are assessed 0, 3, 8, 26 and 52 weeks post-randomization, except for global health change, which is only evaluated at completed ST. This study features the blinding of participants, assessors and therapists, and will improve our understanding of what occurs with the local pathophysiology, tissue morphology and clinical status of persons with KOA up to a year after the initiation of ST and a higher 904 nm LLLT dose than in any published trial on this topic. Full article

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved. Full article

This paper presents the sensor network infrastructure for a home care system that allows long-term monitoring of physiological data and everyday activities. The aim of the proposed system is to allow the elderly to live longer in their home without compromising safety and ensuring the detection of health problems. The system offers the possibility of a virtual visit via a teleoperated robot. During the visit, physiological data and activities occurring during a period of time can be discussed. These data are collected from physiological sensors (e.g., temperature, blood pressure, glucose) and environmental sensors (e.g., motion, bed/chair occupancy, electrical usage). The system can also give alarms if sudden problems occur, like a fall, and warnings based on more long-term trends, such as the deterioration of health being detected. It has been implemented and tested in a test environment and has been deployed in six real homes for a year-long evaluation. The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment, combining the acquisition of sensor data, a flexible and adaptable middleware compliant with the OSGistandard and a context recognition application. The system has been developed in a European project called GiraffPlus. Full article

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect^® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios. Full article