Search Results (49)

Observational astronomy requires specialized equipment and favourable outdoor conditions, creating barriers to access for many enthusiasts. Streaming platforms can help bridge this gap by offering accessible views of celestial events, fostering broader public engagement and educational opportunities. In this paper, we introduce a methodology and a set of tools designed to power a YouTube channel that shares authentic recordings of Deep-Sky Objects, the Sun, the Moon, and planets. Each video is accompanied by detailed information on observation conditions and post-processing steps. The content is structured into two complementary formats: raw footage, captured using smart telescopes, and AI-enhanced videos that highlight specific features or phenomena using custom-trained AI models. Furthermore, the YouTube channel and associated AI tools may serve as a dynamic platform for long-term sky observation, supporting the detection of seasonal patterns and transient celestial events. Full article

Indoor image semantic segmentation technology is applied to fields such as smart homes and indoor security. The challenges faced by semantic segmentation techniques using RGB images and depth maps as data sources include the semantic gap between RGB images and depth maps and the loss of detailed information. To address these issues, a multi-head self-attention mechanism is adopted to adaptively align features of the two modalities and perform feature fusion in both spatial and channel dimensions. Appropriate feature extraction methods are designed according to the different characteristics of RGB images and depth maps. For RGB images, asymmetric convolution is introduced to capture features in the horizontal and vertical directions, enhance short-range information dependence, mitigate the gridding effect of dilated convolution, and introduce criss-cross attention to obtain contextual information from global dependency relationships. On the depth map, a strategy of extracting significant unimodal features from the channel and spatial dimensions is used. A lightweight skip connection module is designed to fuse low-level and high-level features. In addition, since the first layer contains the richest detailed information and the last layer contains rich semantic information, a feature refinement head is designed to fuse the two. The method achieves an mIoU of 53.86% and 51.85% on the NYUDv2 and SUN-RGBD datasets, which is superior to mainstream methods. Full article

Photoelectrochromic devices, which combine light-induced color change with energy-efficient optical modulation, have attracted significant attention for applications such as smart windows, displays, and optical sensors. However, achieving high optical modulation, fast switching speeds, and long-term stability remains a major challenge. In this study, we explore the structural and photoelectrochromic enhancements in tungsten oxide (WO₃) films achieved by doping with molybdenum disulfide quantum dots (MoS₂ QDs) and grapheneoxide–molybdenum disulfide quantum dots (GO–MoS₂ QDs) for advanced photoelectrochromic devices. X-ray diffraction (XRD) analysis revealed that doping with MoS₂ QDs and GO–MoS₂ QDs leads to a reduction in the crystallite size of WO₃, as evidenced by the broadening and decrease in peak intensity. Transmission Electron Microscopy (TEM) confirmed the presence of characteristic lattice fringes with interplanar spacings of 0.36 nm, 0.43 nm, and 0.34 nm, corresponding to the planes of WO₃, MoS₂, and graphene. Energy-Dispersive X-ray Spectroscopy (EDS) mapping indicated a uniform distribution of tungsten, oxygen, molybdenum, and sulfur, suggesting homogeneous doping throughout the WO₃ matrix. Scanning Electron Microscopy (SEM) analysis showed a significant decrease in film thickness from 724.3 nm for pure WO₃ to 578.8 nm for MoS₂ QD-doped WO₃ and 588.7 nm for GO–MoS₂ QD-doped WO₃, attributed to enhanced packing density and structural reorganization. These structural modifications are expected to enhance photoelectrochromic performance by improving charge transport and mechanical stability. Photoelectrochromic performance analysis showed a significant improvement in optical modulation upon incorporating MoS₂ QDs and GO–MoS₂ QDs into the WO₃ matrix, achieving a coloration depth of 56.69% and 70.28% at 630 nm, respectively, within 10 min of 1.5 AM sun illumination, with more than 90% recovery of the initial transmittance within 7 h in dark conditions. Additionally, device stability was improved by the incorporation of GO–MoS₂ QDs into the WO₃ layer. The findings demonstrate that incorporating MoS₂ QDs and GO–MoS₂ QDs effectively modifies the structural properties of WO₃, making it a promising material for high-performance photoelectrochromic applications. Full article

Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR). Over the course of this paper, both algorithms are adapted to localize people in 32 × 32-pixel thermal array images. The drawbacks in precision due to the sparse amount of labeled data were counteracted with a novel generative image generator (IIG). This generator creates synthetic thermal frames from the sparse amount of available labeled data. Multiple robustness tests were performed during the evaluation process to determine the overall usability of the aforementioned algorithms as well as the advantage of the image generator. Both algorithms provide a high mean average precision (mAP) exceeding 98%. They also prove to be robust against disturbances of warm air streams, sun radiation, the replacement of the sensor with an equal type sensor, new persons, cold objects, movements along the image frame border and people standing still. However, the precision decreases for persons wearing thick layers of clothes, such as winter clothing, or in scenarios where the number of present persons exceeds the number of persons the algorithm was trained on. In summary, both algorithms are suitable for detection and localization purposes, although YOLOv5m has the advantage in real-time image processing capabilities, accompanied by a smaller model size and slightly higher precision. Full article

Electric mobility is attracting significant attention in the current era due to its environmental benefits, sustainable transportation options, and the absence of carbon emissions. However, challenges such as the high price of batteries, inefficient charging techniques, and compatibility linking the charging station with electric vehicles (EVs) must be addressed. This article reviews advancements and identifies challenges in charging infrastructure for electric mobility. This study incorporates and analyzes an integrated review of approximately 223 research articles. Current research trends and states of charging infrastructure are prepared as per the Web of Science (WoS) database from 2013 to 2023. In light of recent extensions in wireless power transfer technology, including capacitive, inductive, and magnetic gear topology, are presented to advance the charging infrastructure. Different charging tactics based on power source, such as level-1 AC, level-2 AC, level-3 DC fast, and level-3 DC ultra-rapid charging, related to charging infrastructure are addressed. The vehicle-to-grid (V2G) integration methodology is addressed to construct a smart city by presenting the transfer of power and related data through linkage and moving systems. The exploration of artificial intelligence, global connectivity of electric vehicles (EVs), sun-to-vehicle (S2V), and vehicle-to-everything (V2X) techniques with EVs is conducted to enhance and progress the charging infrastructure. Key barriers associated with charging infrastructure are identified. Full article

Industry 4.0 marks a major technological shift, revolutionizing manufacturing with increased efficiency, productivity, and sustainability. This transformation is paralleled in agriculture through smart farming, employing similar advanced technologies to enhance agricultural practices. Both fields demonstrate a symmetry in their technological approaches. Recent advancements in software engineering and the digital twin paradigm are addressing the challenge of creating embedded software systems for these technologies. Digital twins allow full development of software systems before physical prototypes are made, exemplifying a cost-effective method for Industry 4.0 software development. Our digital twin prototype approach mirrors software operations within a virtual environment, integrating all sensor interfaces to ensure accuracy between emulated and real hardware. In essence, the digital twin prototype acts as a prototype of its physical counterpart, effectively substituting it for automated testing of physical twin software. This paper discusses a case study applying this approach to smart farming, specifically enhancing silage production. We also provide a lab study for independent replication of this approach. The source code for a digital twin prototype of a PiCar-X by SunFounder is available open-source on GitHub, illustrating how digital twins can bridge the gap between virtual simulations and physical operations, highlighting the symmetry between physical and digital twins. Full article

The Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) standard is attracting great interest in various applications such as smart meters, smart cities and Internet of Things (IoT) devices due to the attractive features that the standard offers, such as multihop and mesh topologies, a relatively high data rate, frequency hopping, and interoperability between manufacturers. However, the process of connecting nodes in Wi-SUN FAN networks, which includes discovering, joining, and forming the network, has been shown to be slow, especially in multihop environments, which has motivated research and experimentation to analyze this process. In the existing literature, to measure network formation time, some authors have performed experiments with up to 100 devices, which is a costly and time-consuming methodology. Others have used simulation tools that are difficult to replicate, because little information is available about the methodology used or because they are proprietary. Despite these efforts, there is still a lack of information to adequately assess the formation time of Wi-SUN FAN networks, since the experimental tests reported in the literature are expensive and time-consuming. Therefore, alternatives such as computer simulation have been explored to speed up performance analysis in different scenarios. With this perspective, this paper is focused on the implementation of the Wi-SUN FAN network formation process using the Contiki-NG open source operating system and the Cooja simultor, where a functionality was added that makes it possible to efficiently analyze the network performance, thereby facilitating the implementation of new techniques to reduce network training time. The simulation tool was integrated into Contiki-NG and has been used to estimate the network formation times in various indoor environments. The correspondence between the experimental and numerical results obtained shows that our proposal is efficient to study the formation process of this type of networks. Full article

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality. Full article

Polymer-dispersed liquid crystal (PDLC) film is an active smart film penetrating the market due to its unique functionalities. These functional characteristics include switchable tint capabilities, which shield building residents from the sun’s harmful ultraviolet (UV) rays, improve energy-saving features, and produce higher cost-efficiency. Although PDLC films are promising in several applications, there is still ambiguity on the performance of PDLC films. Particularly, the sizing effects’ (such as film thickness and area) correlation with visible light transmission (VLT), ultraviolet rejection (UVR), infrared rejection (IRR), light intensity, current consumption, and apparent power consumption is not well understood. Therefore, this study investigated the sizing effects of PDLC films, including the thickness effect on VLT, UVR, IRR, light intensity, and area influence on current and apparent power consumptions. The varying applied voltage effect on the light transmittance of the PDLC film was also effectively demonstrated. A 0.1 mm PDLC film was successfully presented as a cost-efficient film with optimal parameters. Consequently, this study paves the way for a clearer understanding of PDLC films (behavior and sizing effects) in implementing economic PDLC films for large-scale adoption in commercial and residential premises. Full article

The sun’s total radiation alone exceeds the world population’s entire energy consumption by 7.500 times and ignites secondary renewable energy sources. The end energy consumption buildings use for heating amounts to 28% of Germany’s total energy consumption. With the ongoing trend of digitalization and the transition of the German energy supply away from fossil fuels and the consequent political dependency, electric heat pumps and photovoltaic (PV) systems have become increasingly important to the discussion. This has led to an increasing demand for smart control strategies, especially for inert systems such as thermally activated building systems (TABS). This paper presents and analyses a weather predictive control (WPC) strategy using a validated thermodynamic simulation model. The literature review of this paper outlines that the current common control strategies are data intense and complex in their implementation into the built environment. The simple approach of the WPC uses future ambient temperature and solar radiation to optimize the control of the heating, cooling, ventilation, and sun protection system. The thermal comfort and energy demand evaluate the concept. We show that with a WPC for TABS, thermal comfort can improve without increasing the energy demand for the office building in the moderate climate of Munich. Furthermore, this paper concludes that the WPC works more effectively with more thermal mass. This simplified building control strategy promotes the European roadmap goal of climate neutrality in 2050, as it bridges the phenomenon of the performance gap. Full article

In the context of the current energy crisis and climate change, the importance of discussions on how to incorporate monument protection into sustainable strategies that mitigate the human impact on the environment and implement renewable sources while preserving cultural values is raised. Through the case study of the Monument Reserve in Bratislava, Slovakia, this article presents the potentials and limits of the integration of photovoltaic systems in historic urban structures that directly affect their feasible participation in smart city and positive energy district concepts by means of energy cooperativeness. This study highlights the most current recommendations and basic principles on how to assess their visual impact and select the most appropriate solutions. Using the datafication process, it analyzes the irradiance of pitched and flat roof polygons of the set area based on their characteristics such as the normal vector azimuth and slope of the rooftops. For this purpose, a 3D morphological model in LOD3 detail and the open-source solar irradiation model r.sun implemented in GRASS GIS / QGIS were used. The data obtained provided an estimate of the output potential to endow the city’s power grid and were compared to the electricity consumption of the particular city district. Furthermore, these data are suitable for designing a customized technical and aesthetic solution for the integration of photovoltaics with respect to cultural sustainability, as well as for decision- and policy makers. Full article

Almost one-third of all malignant melanoma patients exhibit emotional stress indicating the need for professional care. Considering this, patients’ psychological needs are routinely overlooked and unfulfilled, even though there is substantial evidence that psychological therapies may enhance psychosocial outcomes for melanoma patients, such as low mood, sadness, and anxiety. Among developing countries and some health systems in developed regions, the lack of awareness and screening methods for skin cancer creates a high risk of psychological issues associated with more advanced diseases. Therefore, the current study aimed to investigate and compare the impact of malignant melanoma awareness for screening, prevention, and treatment on the patient’s quality of life and coping with stress and depression, based on patients’ age. This cross-sectional study recruited 238 patients with malignant melanoma distributed into two groups, Group A patients between 18 and 65 years and Group B patients older than 65. There were no significant gender differences and cancer staging differences between groups, although self-reported depressed mood and anhedonia were significantly more frequent in younger adults with malignant melanoma (43.8% vs. 28.9%). From the unstandardized surveys, it was observed that significantly fewer patients from Group B knew that melanoma could be caused by sun exposure (34.2% vs. 52.2%), and they were less likely to use sunscreen or visit a doctor to evaluate their skin moles (25.9% vs. 14.5%). Elderly patients preferred television as the main source of information, and only 68.4% of patients from Group B were using smart devices. There was a significantly higher physical score on the SF-12 scale among Group A patients, although patients from Group B scored higher in the mental health assessment, and the perceived helplessness on the PSS-10 scale was significantly higher compared to younger adults with melanoma (2.97 vs. 2.71, p-value = 0.036). Lower scores on the physical and mental SF-12 questionnaire determined a higher presence of depressive symptoms (rho = −0.352, respectively rho = −0.273). Higher scores on the DLQI sexual difficulties and treatment difficulties also correlated significantly with the presence of depressive symptoms and anhedonia (rho = 0.341, respectively rho = 0.264). Awareness campaigns for malignant melanoma should focus on the elderly population, too, using the television as the main communication channel. On the other hand, the more informed and knowledgeable group of adults younger than 65 are more likely to experience psychological problems and should be targeted for psycho-oncological aid. Full article

Photovoltaic (PV) power production is characterized by high variability due to short-term meteorological effects such as cloud movements. These effects have a significant impact on the incident solar irradiance in PV parks. In order to control PV park performance, researchers have focused on Computer Vision and Deep Learning approaches to perform short-term irradiance forecasting using sky images. Motivated by the task of improving PV park control, the current work introduces the Image Regression Module, which produces irradiance values from sky images using image processing methods and Convolutional Neural Networks (CNNs). With the objective of enhancing the performance of CNN models on the task of irradiance estimation and forecasting, we propose an image processing method based on sun localization. Our findings show that the proposed method can consistently improve the accuracy of irradiance values produced by all the CNN models of our study, reducing the Root Mean Square Error by up to 10.44 W/m${}^2$ for the MobileNetV2 model. These findings indicate that future applications which utilize CNNs for irradiance forecasting should identify the position of the sun in the image in order to produce more accurate irradiance values. Moreover, the integration of the proposed models on an edge-oriented Field-Programmable Gate Array (FPGA) towards a smart PV park for the real-time control of PV production emphasizes their advantages. Full article

Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households’ electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution. Full article

One of the modern methods for enhancing the efficiency of photovoltaic (PV) systems is implementing a solar tracking mechanism in order to redirect PV modules toward the sun throughout the day. However, the use of solar trackers increases the system’s electrical consumption, hindering its net generated energy. In this study, a novel self-tracking solar-driven PV system is proposed. The smart solar-driven thermomechanical actuator takes advantage of a solar heat collector (SHC) device, in the form of a parabolic trough solar concentrator (PTC), and smart shape memory alloy (SMA) to produce effective mechanical energy for solar tracking applications from sun rays. Furthermore, a thermal–optical analysis is presented to evaluate the performance of the solar concentrator for the simulated weather condition of Dammam City, Saudi Arabia. The numerical results of the thermal and optical analyses show the promising feasibility of the proposed system in which SMA springs with an activation temperature between 31.09 °C and 45.15 °C can be utilized for the self-tracking operations. The work presented adds to the body of knowledge an advanced SMA-based SHC device for solar-based self-actuation systems, which enables further expansions within modern and advanced solar thermal applications. Full article