Search Results (4,440)

The spacecraft microgravity simulation air-bearing platform is a crucial component of the spacecraft ground testing system. Special disturbances, such as the flatness and roughness of the contact surface between the air bearings and the granite platform, increasingly affect the control accuracy of the simulation experiment as the number of air bearings increases. To address this issue, this paper develops a novel compensation control system based on Active Disturbance Rejection Control (ADRC), which estimates and compensates for the disturbing forces and moments caused by the roughness and levelness of the contact surface, thereby improving the control precision of the spacecraft ground simulation system. A dynamic model of the multi-air-bearing platform under disturbance is established. A cascade ADRC algorithm based on the Linear Extended State Observer (LESO) is designed. The Gauss–Newton iteration method is used to identify the parameters of the sliding friction coefficient and the tilt angle of the air-bearing platform. A full-physics simulation experimental platform for spacecraft with rotor-based propulsion is constructed, and the proposed algorithm is validated. The experimental results show that on a marble surface with a flatness of grade 00, an overall tilt angle of 0–1 degrees, and a surface friction coefficient of 0–0.01, the position control accuracy for the simulated spacecraft can reach 1.5 cm, and the attitude control accuracy can reach 1°. Under ideal conditions, the identification accuracy for the contact surface friction coefficient is 2 × 10⁻⁴, and the recognition accuracy for the overall levelness of the marble surface can reach 1 × 10⁻³, laying the foundation for high-precision ground simulation experiments of spacecraft in multi-air-bearing scenarios. Full article

Buildings account for a substantial portion of global energy use, with about one-third of total consumption attributed to them, according to IEA statistics, significantly contributing to carbon emissions. Building energy efficiency is crucial for combating climate change and achieving energy savings. Smart buildings, leveraging intelligent control systems, optimize energy use to reduce consumption and emissions. Deep reinforcement learning (DRL) algorithms have recently gained attention for heating, ventilation, and air conditioning (HVAC) control in buildings. This paper reviews current research on DRL-based HVAC management and identifies key issues in existing algorithms. We propose an enhanced intelligent building energy management algorithm based on the Soft Actor–Critic (SAC) framework to address these challenges. Our approach employs the distributed soft policy iteration from the Distributional Soft Actor–Critic (DSAC) algorithm to improve action–state return stability. Specifically, we introduce cumulative returns into the SAC framework and recalculate target values, which reduces the loss function. The proposed HVAC control algorithm achieved 24.2% energy savings compared to the baseline SAC algorithm. This study contributes to the development of more energy-efficient HVAC systems in smart buildings, aiding in the fight against climate change and promoting energy savings. Full article

The benefits of ecosystem services provided by urban green systems have been highlighted in research on spatial and landscape planning, and the need has emerged for an integrated approach to urban green planning aiming at increasing climate mitigation and urban resilience. Research indicates that plant selection and substrate management are vital for optimizing the most important performance of green roofs, like building thermal insulation, urban heat reduction, air quality improvement, and stormwater management. In Mediterranean climates, it is essential to investigate sustainable management solutions for green roofs like the growth potential of native, low-maintenance forbs adapted to thermal and water stress on specific substrates. Medicinal species may be suitable, provided that interactions with pollutants are controlled. This study evaluates the performance of Melissa officinalis and Hypericum perforatum on experimental green roof modules under controlled conditions, comparing chemical fertilization and three different treatments with biomass from Trifolium repens used as green manure. The key metrics of fresh and dry biomass, plant cover ratio, and chlorophyll content are measured. Results show significantly higher values of cover and biomass for these two species treated with green manure in comparison to chemical fertilization, with no significant differences in chlorophyll content, indicating that T. repens is a useful source of green manure in green roof management. Overall, the results are consistent with the research goals of suggesting sustainable solutions for green roof management, since low-maintenance vegetation and green manure contribute to the elimination of chemicals in urban green. Full article

Urban sound encompasses various acoustic events, from critical safety-related sound to everyday environmental noise. In response to the need for comprehensive and scalable sound monitoring, this study introduces an integrated system combining the Hierarchical Wireless Acoustic Sensor Network (HWASN) with the new proposed end-to-end CNN-CNN-BiLSTM-Attention (CCBA) sound classification model. HWASN facilitates large-scale, scalable sound data collection and transmission through a multi-hop architecture. At the same time, the CCBA model, optimized for Jetson Nano, delivers high-accuracy classification in noisy environments with minimal computational overhead. The CCBA model is trained using distillation techniques, achieving up to a 71-fold speed-up compared to its teacher system. Real-world deployments demonstrate the system’s robust performance under dynamic acoustic conditions. Combining HWASN’s scalability with CCBA’s classification efficiency provides a versatile and long-term solution for comprehensive urban sound monitoring. Additionally, other environmental parameters, such as air quality, light intensity, temperature, humidity, and atmospheric pressure, are sampled using this system to enhance its application in smart city management, urban planning, and public safety, addressing various modern urban needs. Full article

As a widely used clean energy source, solar energy has demonstrated significant promise across various applications due to its wide spectral range and efficient absorption performance. This study introduces a cross-structured, ultra-broadband solar absorber utilizing titanium (Ti) and titanium dioxide (TiO₂) as its foundational materials. The absorber exhibits over 90% absorption within the 280–4000 nm wavelength range and surpasses 95% absorption in the broader spectrum from 542 to 3833 nm through the cavity coupling effect of incident light excitation and the subsequent initiation of the surface plasmon resonance mechanism, thus successfully achieving the goal of broadband high absorption. Through the finite difference time domain method (FDTD) simulation, the average absorption efficiency reaches 97.38% within the range from 280 nm to 4000 nm, and it is 97.75% in the range from 542 nm to 3833 nm. At the air mass of 1.5 (AM 1.5), the average absorption efficiency of solar energy is 97.46%, and the loss of solar energy is 2.54%, which has extremely high absorption efficiency. In addition, thanks to the material considerations, the absorber adopts a variety of high-temperature resistant materials, making the thermal radiation efficiency in a high-temperature environment still good; specifically, at the temperature of 900 K, its thermal radiation efficiency can reach 97.27%, and at the extreme 1800 K temperature, it can still maintain 97.52% of high efficiency thermal radiation, further highlighting its excellent thermal stability and comprehensive performance. The structure exhibits excellent optical absorption and thermal radiation properties, which give it broad applicability as an ideal absorber or thermal emitter. More importantly, the absorber is insensitive to the polarization state of the light and can effectively handle the incident light lines in the wide-angle range. In addition, its photothermal conversion efficiency (Hereafter referred to as pc efficiency) can sustain an elevated level under various temperature conditions, which enables it to flexibly adapt to diverse environmental conditions, especially suitable for the integration and application of solar photovoltaic systems, and further broaden its potential application range in the field of renewable energy. Full article

Stone mill operations contribute significantly to air pollution and increase health risks not only for workers but also for nearby communities. This study aimed to assess the health impacts of stone mill industries on nearby residents. The research was conducted in two areas: a primary region with a high number of stone mills and an area without stone mills. A questionnaire-based survey was employed, and potential health risks were evaluated using the hazard quotient (HQ) method. Total suspended particulates (TSPs) and particulate matter-10 micron (PM10) were analyzed as hazard factors based on monitoring data from seven stone mills collected between 2008 and 2021. The study found that residents in major stone mill areas reported higher hazard quotients (HQs) than those living farther from the mills, with a statistically significant association (p < 0.01). Seasonal variations also influenced dust distribution, with the highest TSP and PM10 levels recorded during winter, exacerbating health risks for local populations. This study highlights the need for improved community settlement planning, consideration of meteorological conditions, regulatory interventions by relevant agencies, and enhancements in environmental monitoring systems to mitigate the adverse health effects of stone mill operations. Full article

Domestic cooling requirements in arid and hot climate regions present a substantial challenge in minimizing energy consumption and reducing carbon emissions, largely due to the extensive dependence on electricity-intensive air conditioning systems. The limitations and inefficiencies of traditional construction and insulation materials, coupled with their improper application, further intensify the challenges posed by extreme climatic conditions. Considering these challenges, this study thoroughly assesses a novel and unconventional solution recently introduced for improving insulation: mycelium-based thermal insulation. Mycelium is the growth form of filamentous fungi, capable of binding organic matter through a network of hyphal microfilaments. This research utilizes DesignBuilder v7.3.1.003 simulation software to assess the thermal performance of residential buildings that incorporate mycelium as an insulator. The aim is to compare its efficacy with commonly used traditional insulators in Qatar and to investigate the potential of mycelium as an eco-friendly solution for minimizing thermal energy consumption, enhancing thermal comfort, decreasing carbon emissions, and achieving annual thermal energy savings. This study examines various insulation materials and accentuates the unique advantages offered by mycelium-based composites. Simulation results indicate that the placement of mycelium on both the inner and outer surfaces results in significant annual energy savings of 8.11 TWh, accompanied by a substantial reduction in CO₂ emissions. Full article

The Senise red pepper, known as peperone crusco, is a protected geographical indication (PGI) product from Basilicata, Italy, traditionally consumed dried. Producers use semi-open greenhouses to meet PGI standards, but significant losses are caused by rot from microorganisms thriving in high moisture, temperature, and humidity, which also encourage pest infestations. To minimize losses, a low-cost alert system was developed. The study, conducted in summer 2022 and 2023, used external parameters from the ALSIA Senise weather station and internal sensors monitoring the air temperature and humidity inside the greenhouse. Since rot is complex and difficult to model, an artificial intelligence (AI)-based approach was adopted. A feed forward neural network (FFNN) estimated greenhouse climate conditions as if it were empty, comparing them with actual values when peppers were present. This revealed the most critical period was the first 3–4 days after introduction and identified a critical air relative humidity threshold. The system could also predict microclimatic parameters inside the greenhouse with red peppers, issuing warnings one hour before risk conditions arose. In 2023, it was tested by comparing predicted values with previously identified thresholds. When critical levels were exceeded, greenhouse operators were alerted to adjust conditions. In 2023, pepper rot decreased. Full article

Background and Objective: Air pollution poses significant risks to global public health and has well-established links to respiratory diseases. This study investigates the associations between air pollution markers—Air Quality Index (AQI), ambient ozone, and nitrogen dioxide (NO₂)—and the incidence of chronic obstructive pulmonary disease (COPD), asthma, and tuberculosis. It also examines how socioeconomic factors such as gross domestic product (GDP) per capita, tobacco prevalence, and healthcare expenditure influence these relationships. This study includes data from 27 countries, thereby offering a global perspective to inform public health interventions and policy reforms. Methods: Data on average air pollution levels, respiratory disease incidence, and socioeconomic factors were collected from publicly available sources spanning four years. The 27 countries included in the study were selected to represent a broad range of pollution levels, income brackets, and geographical regions. Statistical analyses were performed using Python 3.12.0 to explore the relationships between these variables. Key Findings: AQI and NO₂ levels were significantly associated with increased incidences of COPD and tuberculosis, with rates rising especially during periods of heightened pollution. Conversely, ambient ozone exhibited inconsistent relationships with respiratory diseases, heavily influenced by socioeconomic factors. Higher GDP per capita and healthcare expenditure were linked to improved management of infectious diseases like tuberculosis, though they also corresponded with higher reporting of chronic conditions such as COPD. Tobacco smoking emerged as a critical risk factor for COPD across all regions. Conclusions: This study underscores the strong associations between air pollutants and respiratory diseases, particularly tuberculosis and COPD, with socioeconomic factors significantly influencing these relationships. Reducing air pollution and improving healthcare systems, particularly in low-income regions, are essential to mitigating the global burden of respiratory diseases. Full article

Considering the energy-saving advantages of the direct evaporative cooler (DEC) compared to the traditional air conditioning system (TAC), this study aims to indicate its ability to improve the thermal comfort and the indoor air quality of the bus compared to the bus air-conditioned by the traditional compressor system. Taking a bus in Lanzhou as the object, the numerical model and method were first verified by an experimental method. Then, numerical analyses were simultaneously carried out in both bus models, which were air-conditioned by TAC and DEC, respectively. The results showed that the thermal comfort of the bus air-conditioned by DEC is more satisfactory, and the indoor air quality is better. Additionally, the bus air-conditioned by DEC achieves a 43.7% improvement in the temperature efficiency and a 31.3% improvement in the ventilation efficiency compared to the bus air-conditioned by TAC. The conclusion will provide valuable insights into the application of DEC in buses in dry regions. Full article

The interference of human activities in water bodies has contributed to a deterioration in water quality. With the advancement of the Internet of Things (IoT), aided by transmission technologies such as LoRa (Long Range), low-cost solutions have emerged for long-distance environment monitoring scenarios. One key challenge in such IoT-based systems is selecting LoRa transmission parameters to ensure efficient data exchange among nodes, adapting to varying network conditions. Well-known strategies adapt transmission parameters according to network context through information exchange among nodes and LoRa gateway(s). In this work, we introduce a novel LoRa parameter selection algorithm by incorporating three major LoRa metrics (RSSI, SNR, and PDR) and conducting a comprehensive characterization and validation in the forest environment to build a set of reference values of transmission quality, which are employed in a binary search methodology, utilizing the R-array, representing the transmission quality according to LoRa parameters. The experimental results indicate that the proposed algorithm achieves a 16.20% reduction in Time on Air (ToA). Furthermore, our algorithm optimized the transmission power (TP) selection, achieving at least 38% lower energy consumption than ADR TP parameters. These results highlight that our proposed algorithm can enhance the transmissions in a rainforest environment. Full article

Observations of Very High Energy (VHE) gamma ray sources using the ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs) play a pivotal role in understanding the non-thermal energetic phenomena and acceleration processes under extreme astrophysical conditions. However, detection of the VHE gamma ray signal from the astrophysical sources is very challenging, as these telescopes detect the photons indirectly by measuring the flash of Cherenkov light from the Extensive Air Showers (EAS) initiated by the cosmic gamma rays in the Earth’s atmosphere. This requires fast detection systems, along with advanced data acquisition and analysis techniques to measure the development of extensive air showers and the subsequent segregation of gamma ray events from the huge cosmic ray background, followed by the physics analysis of the signal. Here, we report the development of a python-based package for analyzing the data from the Major Atmospheric Cherenkov Experiment (MACE), which is operational at Hanle in India. The Python-based MACE data Analysis Package (PyMAP) analyzes data by using advanced methods and machine learning algorithms. Data recorded by the MACE telescope are passed through different utilities developed in the PyMAP to extract the gamma ray signal from a given source direction. We also propose a new image cleaning method called DIOS (Denoising Image of Shower) and compare its performance with the standard image cleaning method. The working performance of DIOS indicates an advantage over the standard method with an improvement of ≈$25\%$ in the sensitivity of MACE. Full article

The energy consumption of data center cooling systems is rapidly increasing, necessitating urgent improvements in cooling system performance. This study investigates a pump-driven two-phase cooling system (PTCS) utilizing a parallel-flow heat exchanger (PFHE) as an evaporator, positioned at the rear of server cabinets. The findings indicate that enhancing the vapor quality at the PFHE outlet improves the overall cooling performance. However, airflow non-uniformity induces premature localized overheating, restricting further increases in vapor quality. For PFHEs operating with a two-phase outlet condition, inlet air temperature non-uniformity has a relatively minor impact on the cooling capacity but significantly affects the drop in pressure. Specifically, higher upstream air temperatures increase the pressure drop by 7%, whereas higher downstream air temperatures reduce it by 7.7%. The implementation of multi-pass configurations effectively mitigates localized overheating caused by airflow non-uniformity, suppresses the decline in cooling capacity, and enhances the operational vapor quality of the cooling system. Full article

To increase energy efficiency, heat pump dryers and membrane dryers have been proposed to replace conventional fossil fuel dryers. Both conventional and heat pump dryers require substantial energy for condensing and reheating, while “active” membrane systems require vacuum pumps that are insufficiently developed. Lower temperature dehumidification systems make efficient use of membrane energy recovery ventilators (MERVs) that do not need vacuum pumps, but their high heat losses and lack of vapor selectivity have prevented their use in industrial drying. In this work, we propose an insulating membrane energy recovery ventilator for moisture removal from drying exhaust air, thereby reducing sensible heat loss from the dehumidification process and reheating energy. The second law analysis of the proposed system is carried out and compared with a baseline convective heat pump dryer. Irreversibilities in each component under different ambient temperatures (5–35 °C) and relative humidity (5–95%) are identified. At an ambient temperature of 35 °C, the proposed system substantially reduces sensible heat loss (47–60%) in the dehumidification process, resulting in a large reduction in condenser load (45–50%) compared to the baseline system. The evaporator in the proposed system accounts for up to 59% less irreversibility than the baseline system. A maximum of 24.5% reduction in overall exergy input is also observed. The highest exergy efficiency of 10.2% is obtained at an ambient condition of 35 °C and 5% relative humidity, which is more than twice the efficiency of the baseline system under the same operating condition. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into smart city infrastructures necessitates advanced security measures to ensure their safe and sustainable operation. However, existing Automatic Dependent Surveillance–Broadcast (ADS-B) systems are highly vulnerable to spoofing, data falsification, and cyber threats, which compromises air traffic management and poses significant challenges to UAV security. This paper presents an innovative approach to improving UAV security by introducing a novel steganographic method for ADS-B data protection. The proposed method leverages Fourier transformation to embed UAV identifiers into ADS-B signals, ensuring a high level of concealment and robustness against signal distortions. A key feature of the approach is the dynamic parameter management system, which adapts to varying transmission conditions to minimize distortions and enhance resilience. Experimental validation demonstrates that the method achieves a tenfold reduction in Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) compared to existing techniques such as mp3stego while also improving the Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) compared to s-tools. The proposed solution ensures compliance with existing ADS-B standards, maintaining seamless integration with air traffic management systems while enhancing cybersecurity measures. By safeguarding UAV communications, the method contributes to the sustainable development of smart cities and supports critical applications such as logistics, environmental monitoring, and emergency response operations. These findings confirm the practical feasibility of the proposed approach and its potential to strengthen UAV security and ADS-B data protection, ultimately contributing to the resilience and sustainability of urban airspace infrastructure. Full article