Search Results (1,841)

Given the persistently high incidence of skin cancer, there is a need for prevention-focused information on the impact of long-term changes in ambient solar ultraviolet radiation (UVR) on human personal radiation exposure. The exposure categories of the UV Index linked to protection recommendations show long-term shifts in the frequency of occurrence with regional differences in direction and magnitude. The patterns of change for sites in the humid continental climate differ from those for sites in other climate zones such as the humid temperate or Mediterranean climate. The diversity of the individual exposures of indoor and outdoor workers can be described using probability models for personal erythema-effective UVR dose (UVD). For people who work indoors, the largest share of the total individual annual UVD is due to vacation, whereas for people who work outdoors, it is occupational exposure. The change in ambient UVDs at the residential locations is only partially reflected in the individual UVDs. For eight selected European sites between 38° and 60° northern latitude, the median of the individual annual total UVD (excluding travel) during the period 2009–2019 is 0.2 to 2.0% higher for indoor workers and 0.6 to 3.2% higher for outdoor workers compared to the period 1983–2008. Changes in the choice of an exemplary holiday destination offer both indoor and outdoor workers the potential to compensate for the observed long-term trend at their place of residence and work. Full article

The integration of artificial intelligence into daily life significantly enhances the autonomy and quality of life of visually impaired individuals. This paper introduces the Visual Impairment Spatial Awareness (VISA) system, designed to holistically assist visually impaired users in indoor activities through a structured, multi-level approach. At the foundational level, the system employs augmented reality (AR) markers for indoor positioning, neural networks for advanced object detection and tracking, and depth information for precise object localization. At the intermediate level, it integrates data from these technologies to aid in complex navigational tasks such as obstacle avoidance and pathfinding. The advanced level synthesizes these capabilities to enhance spatial awareness, enabling users to navigate complex environments and locate specific items. The VISA system exhibits an efficient human–machine interface (HMI), incorporating text-to-speech and speech-to-text technologies for natural and intuitive communication. Evaluations in simulated real-world environments demonstrate that the system allows users to interact naturally and with minimal effort. Our experimental results confirm that the VISA system efficiently assists visually impaired users in indoor navigation, object detection and localization, and label and text recognition, thereby significantly enhancing their spatial awareness and independence. Full article

Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas. Full article

Accurate indoor positioning is essential for many applications. However, current methods often fall short in complex environments due to signal fluctuations. We propose a new indoor positioning approach, that is, improved sequential deep learning (ISDL), to address this issue. First, we apply sequential classification algorithms to progressively narrow the search space, reducing potential location regions into smaller neighborhoods. Next, we combine a deep neural network (DNN) with Weighted K-Nearest Neighbors (WKNN) to refine the final location prediction. Then, we validate our method using the publicly available $UJIndoorLoc$ dataset, demonstrating superior accuracy compared to existing methods. Specifically, we achieved 95% floor prediction accuracy and reduced the average positioning error to just 7.82 m. By combining sequential classification and the DNN-WKNN hybrid model, we achieve better localization in complex indoor environments. This system offers practical improvements for real-time location-based services and other applications requiring precise indoor positioning. Full article

The Galapagos Islands are undeniably a highly attractive ecosystem for scientists worldwide. However, the energy efficiency and sustainability aspects of their building stock have not yet been studied in depth, which directly hinders the achievement of sustainability goals for the Archipelago, such as reducing resource consumption, minimizing emissions, and improving overall comfort in buildings. Addressing these issues is critical to preserving the islands’ unique ecosystem, as current construction practices are unsustainable and exacerbate environmental pressures, causing over-consumption of local resources and upsetting the delicate ecological balance that sustains this fragile environment. In line with the National Energy Efficiency Plan promoted by the Government of Ecuador for the Archipelago, this study provides transparent and reliable information and data on the building stock of the islands. This work quantifies the impact of buildings on the use of resources and analyses the potential savings of different strategies for reducing greenhouse gas emissions. Various representative typologies are established based on the collection of architectural, construction, and usage information. For each of these typologies, various energy models are developed to establish the baseline and to analyse the demand and comfort of the buildings under different renovation scenarios in order to validate the sustainable construction strategies to be implemented. Moreover, new standards are also defined in order to reduce energy and water consumption and increase indoor air quality and comfort in buildings. In an attempt to generate evidence and facilitate the replication and implementation of sustainable construction standards, three Living Labs (LLs) are created to validate different strategies and technological solutions in different locations, according to the defined standards: a school in Santa Cruz, a hotel in Isabela, and a residential building in San Cristóbal. The findings highlight the effectiveness of specific energy-saving strategies and water conservation measures validated through Living Labs implemented in different locations across the islands. Furthermore, the knowledge generated is transferred through local training of the agents of the construction chain and administration. Full article

EU regulations get stricter from 2028 on by imposing net-zero energy building (NZEB) standards on new residential buildings including on-site renewable energy integration. Heat pumps (HP) using thermal building mass, and Model Predictive Control (MPC) provide a viable solution to this problem. However, the MPC potential in NZEBs considering the impact on indoor comfort have not yet been investigated comprehensively. Therefore, we present a co-simulative approach combining MPC optimization and IDA ICE building simulation. The demand response (DR) potential of a ground-source HP and the long-term indoor comfort in an NZEB located in Vorarlberg, Austria over a one year period are investigated. Optimization is performed using Mixed-Integer Linear Programming (MILP) based on a simplified RC model. The HP in the building simulation is controlled by power signals obtained from the optimization. The investigation shows reductions in electricity costs of up to 49% for the HP and up to 5% for the building, as well as increases in PV self-consumption and the self-sufficiency ratio by up to 4% pt., respectively, in two distinct optimization scenarios. Consequently, the grid consumption decreased by up to 5%. Moreover, compared to the reference PI controller, the MPC scenarios enhanced indoor comfort by reducing room temperature fluctuations and lowering the average percentage of people dissatisfied by 1% pt., resulting in more stable indoor conditions. Especially precooling strategies mitigated overheating risks in summer and ensured indoor comfort according to EN 16798-1 class II standards. Full article

This study analyzes the shortcomings of South Korea’s current evaluation method of ventilation performance for apartment buildings and suggests improvements. The current Korean regulations rely on the air change rate method, which is a prescriptive method and thus inadequately measures indoor air quality practically. Therefore, this study reviews various standards, finding that these standards can be categorized into those evaluating the mechanical performance of ventilators and those assessing indoor ventilation performance. This study highlights that the standards evaluating indoor ventilation performance are based on the tracer gas method but lack clear testing procedures and boundary conditions. This research also reviews the various previous research articles, noting that Korean research places emphasis on system design parameters, while international research focuses on architectural factors. It also identifies inconsistencies in the experimental setups across studies. To improve the current evaluation methods, the research suggests enhancing the tracer gas method with clear testing procedures and introducing indicators such as the age of air and uniformity coefficient together. Since the air change rate method does not consider the actual airflows and distribution in indoor spaces, this method is limited to deriving improvements in indoor ventilation performance. However, the suggested tracer gas method and indicators can be used to discover the optimal locations of vents for better indoor air quality or to drive a better building design to achieve better indoor ventilation performance. In other words, these enhancements aim to provide more accurate and comprehensive insights into the effectiveness of indoor ventilation systems, helping engineers, designers, and residents better understand and improve indoor air quality. Full article

Air pollution causes millions of premature deaths per year due to its strong association with several diseases and respiratory afflictions. Consequently, air quality monitoring and forecasting systems have been deployed in large urban areas. However, those systems forecast outdoor air quality while people living in relatively large cities spend most of their time indoors. Hence, this work proposes an indoor air quality forecasting system, which was trained with data from Mexico City, and that is supported by deep learning architectures. The novelty of our work is that we forecast an indoor air quality index, taking into account seasonal data for multiple horizons in terms of minutes; whereas related work mostly focuses on forecasting concentration levels of pollutants for a single and relatively large forecasting horizon, using data from a short period of time. To find the best forecasting model, we conducted extensive experimentation involving 133 deep learning models. The deep learning architectures explored were multilayer perceptrons, long short-term memory neural networks, 1-dimension convolutional neural networks, and hybrid architectures, from which LSTM rose as the best-performing architecture. The models were trained using (i) outdoor air pollution data, (ii) publicly available weather data, and (iii) data collected from an indoor air quality sensor that was installed in a house located in a central neighborhood of Mexico City for 17 months. Our empirical results show that deep learning models can forecast an indoor air quality index based on outdoor concentration levels of pollutants in conjunction with indoor and outdoor meteorological variables. In addition, our findings show that the proposed method performs with a mean squared error of 0.0179 and a mean absolute error of 0.1038. We also noticed that 5 months of historical data are enough for accurate training of the forecast models, and that shallow models with around 50,000 parameters have enough predicting power for this task. Full article

Caseous lymphadenitis is an infectious disease that has a significant economic impact on sheep breeding. The objectives of this study were to evaluate the effect of season, animals’ age, sex, body score and shearing on the clinical incidence of caseous lymphadenitis, relapses and abscess location in sheep from Settat province, Morocco. In this longitudinal study, 274 clinically healthy sheep were recruited in six flocks among 1451 inspected sheep and followed during 12 months to cover four seasons. The animals involved in this study were assigned to different age categories at continuous recruitment from February to July 2021 (lambs younger than 6 months, young sheep between 6 and 24 months and old sheep more than 24 months) and to sex categories. Selected animals were clinically examined every 2 months to detect superficial abscesses by palpation of superficial lymph nodes. Information about the number of abscesses and their locations was recorded and analyzed in multivariable statistical models. The results showed that 185/274 (67.5%) of the monitored animals developed superficial CL abscesses. The risk ratio (RR) of superficial CL was significantly higher between April and September compared to between January and March (RR~4.4; p < 0.0001). No significant difference was found between October and December compared to between January and March (RR = 1.2; p = 0.64). Regarding the effect of age, the results revealed that the RR was significantly lower in old sheep compared to lambs (RR = 0.45, p < 0.0001). No significant difference was detected between lambs and young sheep (RR = 0.7, p = 0.07). The prevalence of caudally located abscesses (prefemoral and popliteal lymph nodes, as well as in testicles, scrotum and mammary glands) was significantly higher in old sheep than in lambs (20% versus 3%; odds ratio = 7.8, p = 0.02). The sex, body score and shearing since the last examination did not show any significant effect on CL incidence (p > 0.1). Abscess relapse was significantly lower in old sheep than in lambs (IRR = 0.4, p = 0.003). The highest clinical CL incidence was observed in young animals between April and September and was likely due to indoor intensive rearing management. To conclude, both season and age had significant effects on superficial CL incidence. Farmers, veterinarians and technicians should focus attention and preventive measures against CL on young animals during spring and summer. Full article

This study investigated the effects of film hole diameter and soil bulk density on the unidirectional intersection infiltration laws of muddy water fertilization film hole irrigation. Indoor soil box infiltration experiments were conducted. The thickness of the sediment layer, cumulative infiltration amount per unit area, vertical wetting front transport distance, moisture distribution in the wetting body, and nitrate and ammonium nitrogen transport laws were observed and analyzed. The results indicated that both the thickness of the sediment layer and the cumulative infiltration per unit area are inversely correlated with film hole diameter and soil bulk density. Conversely, the vertical wetting front transport distance and nitrogen content are positively correlated with film hole diameter, while exhibiting a negative correlation with soil bulk density. Notably, the initial point of intersection for the moist body was located below the soil surface, with the peak vertical soil moisture content at the intersection approximately 1.5 cm beneath the surface. The distribution pattern of soil nitrate nitrogen at the conclusion of infiltration mirrored that of water content, characterized by a sharp decline near the wetting front. In contrast, soil ammonium nitrogen content decreased significantly in the shallow soil layer as soil depth increased, without a corresponding abrupt decrease near the wetting front. These findings may provide a theoretical foundation for future research on the intersection infiltration laws of muddy water fertilization through film hole irrigation. Full article

Indoor positioning systems have become increasingly important due to the rapid expansion of Internet of Things (IoT) technologies, especially for providing precise location-based services in complex environments such as multi-floor campus buildings. This paper presents a WiFi fingerprint indoor localization system based on AdaBoost, combined with a new access point (AP) filtering technique. The system comprises offline and online phases. During the offline phase, a fingerprint database is created using received signal strength (RSS) values for two four-floor campus buildings. In the online phase, the AdaBoost classifier is used to accurately estimate locations. To improve localization accuracy, APs that always appear in the measurement data are selected for applying the AdaBoost algorithm, aiming to eliminate noise from the fingerprint database. The performance of the proposed method is compared with other well-known machine learning-based positioning algorithms in terms of positioning accuracy and error distances. The results indicate that the average positioning accuracy of the proposed scheme reaches 95.55%, which represents an improvement of 5.55% to 16.21% over the other methods. Additionally, the two-dimensional positioning error can be reduced to 0.25 m. Full article

More than 80% of open-pit coal mines in China are located in northern regions, and the mechanical properties and stability of loose soil–rock mixtures in waste disposal sites are significantly affected by freeze–thaw effects. This article takes the external dumping site of the Baorixile open-pit coal mine in the northern high-altitude region of the Inner Mongolia Autonomous Region as the research object. Through on-site investigation and sampling, indoor triaxial tests (confining pressures of 100 KPa, 200 KPa, and 300 Kpa; moisture contents of 18%, 21%, and 24%), numerical simulation, and other methods, the mechanical properties of soil–rock mixtures in the dumping site under different freeze–thaw cycle conditions were tested to reveal the specific influence of the number of freeze–thaw cycles on the mechanical properties of soil–rock mixtures. Using the discrete element software PFC, the microstructural changes in soil–rock mixtures formed by freeze–thaw cycles were studied, and the deformation mechanism and slip mode of loose slopes in waste disposal sites under different freeze–thaw cycle conditions were explored. The relationship between the number of freeze–thaw cycles and slope stability was clarified. The following conclusions can be drawn: the compressive strength of soil–rock mixtures decreases as a quadratic function with increasing freeze–thaw cycles; as the number of freeze–thaw cycles increases, the internal cracks of the soil–rock mixture model increase exponentially; and as the number of freeze–thaw cycles increases, the stability of the slope in the dumping site decreases significantly, and this stability also decreases with an increase in dumping height. Full article

Advancements in 3D modelling technology have facilitated more immersive and efficient solutions in spatial planning and user-centred design. In healthcare systems, 3D modelling is beneficial in various applications, such as emergency evacuation, pathfinding, and localization. These models support the fast and efficient planning of evacuation routes, ensuring the safety of patients, staff, and visitors, and guiding them in cases of emergency. To improve urban modelling and planning, 3D representation and analysis are used. Considering the advantages of 3D modelling, this study proposes a framework for 3D indoor navigation and employs a multiphase methodology to enhance spatial planning and user experience. Our approach combines state-of-the art GIS technology with a 3D hybrid model. The proposed framework incorporates federated learning (FL) along with edge computing and Internet of Things (IoT) devices to achieve accurate floor-level localization and navigation. In the first phase of the methodology, Quantum Geographic Information System (QGIS) software was used to create a 3D model of the building’s architectural details, which are required for efficient indoor navigation during emergency evacuations in healthcare systems. In the second phase, the 3D model and an FL-based recurrent neural network (RNN) technique were utilized to achieve real-time indoor positioning. This method resulted in highly precise outcomes, attaining an accuracy rate over 99% at distances of no less than 10 metres. Continuous monitoring and effective pathfinding ensure that users can navigate safely and effectively during emergencies. IoT devices were connected with the building’s navigation software in Phase 3. As per the performed analysis, it was observed that the proposed framework provided 98.7% routing accuracy between different locations during emergency situations. By improving safety, building accessibility, and energy efficiency, this research addresses the health and environmental impacts of modern technologies. Full article

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average. Full article

Background: Contaminated air can have a negative impact on patient recovery, leading to longer hospital stays, higher healthcare costs, and even death. Objective: Our study focuses on improving indoor air quality for patient recovery in healthcare facilities. Methods: We conducted computational analysis using the finite element modeling (FEM) technique to investigate the flow of contaminated air exhaled by a patient. Distinct models were examined: a neonatal intensive care unit (NICU) with two-beds and a coronavirus isolation room (CIR). Using ANSYS, we designed models using actual and real specifications of both NICUs and IRs from local hospitals. We determined the optimal dimensions and locations of outlet vents in NICUs and CIRs using simulations with ANSYS software drawing on our designed modeling of air flow. Outlet vent dimensions and locations were modified to achieve optimal air flow for quickly venting out contaminated air from a patient in a room. Results: The results show a substantial improvement in directly venting out the contaminated air from the patient. Conclusions: It can be concluded that the optimal design of outlet vent locations and dimensions using ANSYS simulation results in finding the optimal path for the quick removal of contaminated air flow from the patient in an NICU and CIR. Full article