Open Access

Destination image is a crucial aspect of tourism research. Although extensively studied, recent research highlights the need to explore residents' views and non-visitors' perceptions of destinations. This study aims to address this gap by contrasting Resident Destination Image (RDI) with Tourist Destination Image (TDI) among non-visitors, using Uzbekistan as a case study. The research investigates how Uzbekistan is perceived by its residents in Samarkand and non-visitors in Germany, employing a mixed-method approach of surveys, focus group discussions, and observations. Findings reveal a significant divergence between the positive self-perception of residents and the often unclear or negative image held by non-visitors. The study underscores the influence of stereotypes on non-visitors' perceptions and the need for targeted marketing to bridge the gap between RDI and TDI to unlock the country's untapped tourism potential. The results suggest that enhancing the destination's image through informed marketing strategies can attract more international tourists and support the country's tourism development.

Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.

This paper introduces a transformative “living” hypothesis in architecture and engineering, proposing a paradigm shift from conventional design to regenerative, ecologically interconnected resilient systems. At the heart of our hypothesis is the integration of earth-bound materials and bioreceptive surfaces through metabolic exchanges that can be directly monitored via bioelectricity using advanced computational models and cooperative governance structures. This innovative approach that links the living world with natural materials and digital computing, aims to foster sustainable urban development that dynamically and meaningfully responds to ecological shifts, thereby enhancing social sustainability and environmental resilience. Founded on an active relationship with Earth Based Materials (EBMs) our work operationalises the foundational link between organic life and inorganic matter, e.g., minerals, to establish a dynamic relationship between building materials, and ecological systems drawing on the foundational metabolisms of microbes. To enable this ambitious synthesis, our work builds upon and diverges from traditional foundations by operationalizing actor-network theory, new materialism, and regenerative design principles through the application of bioelectrical microbes to “living” materials and digital twins. We propose a novel resilience framework that not only advocates for a symbiotic relationship between human habitats and natural ecosystems but also outlines practical pathways for the creation of adaptive, self-organizing built environments that are informed by data collection and metabolic feedback loops. These environments are fundamentally regenerative, dynamic, and environmentally responsive in ways that can be understood and engaged by human engineers and designers, transcending current sustainability and resilience targets through a methodology rooted in interdisciplinary collaboration. We address challenges such as regulatory barriers, lack of standardization, and perceptions of inferiority compared to conventional materials, proposing a new standardization framework adaptable to the unique properties of these materials. Our vision is supported by advanced predictive digital modelling techniques and sensors, including the integration of biofilms that generate action potentials, enabling the development of Digital Twins that respond to metabolic signals to enhance sustainability, biodiversity, and ultimately generate environmentally positive socio-economic outcomes. This paper reviews existing methodologies to establish an overview of state-of-the-art developments and offers a clear, actionable plan and recommendations for the realization of regenerative and resilient systems in urban development. It contributes a unique perspective on sustainable urban development, emphasizing the need for a holistic approach, which integrates the foundational metabolism of microbes, assisted by big biological data and artificial intelligences that act in concert to respect both the environment and the intricate dynamics of living systems.

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.

Apnea is a sleep disorder characterized by breathing interruptions during sleep, impacting cardiorespiratory function and overall health. Traditional diagnostic methods, like polysomnography (PSG), are unobtrusive, leading to noninvasive monitoring. This study aims to develop and validate a novel sleep monitoring system using noninvasive sensor technology to estimate cardiorespiratory parameters and detect sleep apnea. We designed a seamless monitoring system integrating noncontact force-sensitive resistor sensors to collect ballistocardiogram signals associated with cardiorespiratory activity. We enhanced the sensor’s sensitivity and reduced the noise by designing a new concept of edge-measuring sensor using a hemisphere dome and mechanical hanger to distribute the force and mechanically amplify the micromovement caused by cardiac and respiration activities. In total, we deployed three edge-measuring sensors, two deployed under the thoracic and one under the abdominal regions. The system is supported with onboard signal preprocessing in multiple physical layers deployed under the mattress. We collected the data in four sleeping positions from 16 subjects and analyzed them using ensemble empirical mode decomposition (EMD) to avoid frequency mixing. We also developed an adaptive thresholding method to identify sleep apnea. The error was reduced to 3.98 and 1.43 beats/min (BPM) in heart rate (HR) and respiration estimation, respectively. The apnea was detected with an accuracy of 87%. We optimized the system such that only one edge-measuring sensor can measure the cardiorespiratory parameters. Such a reduction in the complexity and simplification of the instruction of use shows excellent potential for in-home and continuous monitoring.