Search Results (1,368)

This study systematically evaluates the thermal and humidity control performance of polymer-dispersed liquid crystal (PDLC) smart windows in an operational subtropical commercial building. Conducted from September to November 2025 at the China Railway Construction Building in Zhuhai, China, the field experiment compared four configurations: conventional curtains (fully deployed and fully retracted, respectively) and PDLC film in transparent and opaque states. Results demonstrate that during the high-solar-radiation period (September–October), PDLC in the opaque state exhibited superior thermal control, limiting interior temperature increases to only 2% of the magnitude observed in the transparent state and yielding a maximum interior surface temperature difference of 1.88 °C during peak solar hours (14:00 to 17:00). Humidity fluctuations remained exceptionally stable at ±1.5% in frosted state, significantly outperforming traditional curtain systems (±5.1% to ±8.9%). During November’s transitional climate, the frosted state continued providing thermal buffering, reducing indoor temperature rise by approximately 0.37 °C compared to the transparent state, while the transparent configuration maintained relative humidity approximately 0.5% higher—potentially beneficial for mitigating winter dryness. Cross-seasonal analysis revealed a 57% reduction in indoor temperature rise (from 3.06 °C to 1.31 °C) between September–October and November, directly attributable to seasonal variations in solar geometry. These findings confirm PDLC smart windows’ ability to dynamically regulate temperature, humidity, and daylighting across different seasonal conditions. Despite limitations including non-uniform room geometries and single-climate validation, this research establishes PDLC technology as a promising solution for energy-efficient building envelopes in subtropical regions. Future work should focus on standardized comparative testing, multi-climate validation, long-term durability assessment, and integration with building automation systems. Full article

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

The operation status of transmission line insulators, such as damage, zero-value, pollution, and deterioration, affect the safe operation of power grids. Non-contact detection technology judges the operating status of transmission line insulators through indirect means such as electrical, thermal, acoustic, and image signals. Due to its advantages of rapidity and high efficiency, it has been widely accepted by operation departments. This paper summarizes existing non-contact detection technologies for transmission line insulator conditions, including acoustic wave detection, electric field detection, infrared/ultraviolet imaging detection and spectral detection. It analyzes the principle, characteristics, and application scenarios of each non-contact detection technology. Combined with the rapid development of artificial intelligence technology, the paper looks forward to future new detection methods, such as those integrating deep learning, multi-component comprehensive detection, and multi-source data-driven detection. Finally, the challenges faced by the detection of Ultra-High Voltage (UHV) transmission lines are analyzed. This study provides a reference for the research and development of non-contact detection technology for transmission line insulators. Full article

The relentless rise in global plastic consumption has intensified the challenge of managing plastic waste pollution. Current conventional recycling technologies face significant limitations in processing efficiency and environmental compatibility, hindering the effective recovery of plastic resources. Against this background, microwave pyrolysis technology has emerged as a promising solution, leveraging its dual advantages of thermal and non-thermal effects. This technology enables uniform and rapid heating, substantially reducing processing time and energy consumption. Its characteristics open new pathways for the high-value conversion of waste plastics. Through this approach, waste plastics can be efficiently transformed into valuable products such as pyrolysis oil, hydrogen gas, and solid carbon, demonstrating broad application prospects. This paper first systematically reviews the shortcomings of existing plastic pyrolysis technologies. It then delves into the operational mechanisms, process characteristics, and key influencing factors of microwave-assisted pyrolysis. Finally, it examines current challenges and issues while outlining future research directions, offering insights for the sustainable resource utilisation of waste plastics. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

With the increasingly prominent demand for building energy efficiency and occupant-centric design, accurate and reliable personal thermal comfort models (PTCMs) are playing an important role in various residential and energy applications (e.g., building energy-saving design, indoor environmental regulation, and health and well-being improvement). In recent years, data-driven and artificial intelligence (AI) technologies have attracted considerable attention in the field of personal thermal comfort modeling. This study systematically reviews recent progress in data-driven personal thermal comfort modeling, emphasizing contact-based and non-contact data collection ways, correlation analysis of feature data, modeling methods based on machine learning and deep learning. Considering the high cost and limited scale of collection experiments, as well as noise, ambiguity, and individual differences in subjective feedback, special attention is put on the data-efficient thermal comfort modeling in data scarcity scenarios using a transfer learning (TL) strategy. Characteristics and suitable occasions of four transfer methods (model-based, instance-based, feature-based, and ensemble methods) are summarized to provide a deep perspective for practical applications. Furthermore, integration of PTCM into building environment control is summarized from aspects of the integration framework, modeling method, control strategy, actuator, and control effect. Performance of the integrated systems is analyzed in terms of improving personal thermal comfort and promoting building energy efficiency. Finally, several challenges faced by PTCMs and future directions are discussed. Full article

The traditional ultrasonic bonding technique for IGBT T2 copper terminals often causes physical damage to ceramic substrates, severely compromising the reliability of power modules. Meanwhile, T2 copper laser welding faces inherent challenges including low laser absorption efficiency and unstable molten pool dynamics. To address these issues, this study targets the high-quality connection of IGBT T2 copper terminals and proposes a welding solution integrating a Fiber-Diode Hybrid Laser system with galvo-scanning technology. Comparative experiments between galvo-scanning and traditional oscillation methods CNC scanning were conducted under sinusoidal and circular trajectories to explore the regulation mechanism of welding quality. The results demonstrate that CNC scanning lacks precision in thermal input control, resulting in inconsistent welding quality. Galvo-scanning enables precise modulation of laser energy distribution and molten pool behavior, effectively reducing spatter and porosity defects. It also promotes the transition from columnar grains to equiaxed grains, significantly refining the weld microstructure. Under the sinusoidal trajectory with a welding speed of 20 mm/s, the Lap-shear strength of the galvo-scanned joint reaches 277 N/mm², outperforming all CNC-scanned joints. This research proposes a non-contact welding strategy targeted at eliminating the mechanical failure mechanism associated with conventional ultrasonic bonding of ceramic substrates. It establishes the superiority of galvo-scanning for precision welding of high-reflectivity materials and lays a foundation for its potential application in new energy vehicle power modules and microelectronic packaging. Full article

The detection of trace nitrogen dioxide (NO₂) is critical for environmental monitoring and industrial safety. Among various sensing technologies, chemiresistive sensors based on semiconducting metal oxides are prominent due to their high sensitivity and fast response. However, their application is hindered by inherent limitations, including low selectivity and elevated operating temperatures, which increase power consumption. Two-dimensional metal oxysulfides have recently attracted attention as room-temperature sensing materials due to their unique electronic properties and fully reversible sensing performance. Meanwhile, their combination with optoelectronic gas sensing has emerged as a promising solution, combining higher efficiency with minimal energy requirements. In this work, we introduce non-layered 2D indium oxysulfide (In₂S_xO_3−x) synthesized via a two-step process: liquid metal printing of indium followed by thermal annealing of the resulting In₂O₃ in a H₂S atmosphere at 300 °C. The synthesized material is characterized by a micrometer-scale lateral dimension with 6.3 nm thickness and remaining n-type semiconducting behavior with a bandgap of 2.53 eV. It demonstrates a significant response factor of 1.2 toward 10 ppm NO₂ under blue light illumination at room temperature. The sensor exhibits a linear response across a low concentration range of 0.1 to 10 ppm, alongside greatly improved reversibility, selectivity, and sensitivity. This study successfully optimizes the application of 2D metal oxysulfide and presents its potential for the development of energy-efficient NO₂ sensing systems. Full article

Plasma-activated water (PAW) has gained attention across agricultural, medical, cosmetic, and sterilization fields due to its production of reactive oxygen and nitrogen species (ROS and RNS). Although PAW has been primarily explored for seed germination and sterilization in agriculture, its role as a nutrient source and physiological regulator remains less understood. In this study, PAW generated by a surface dielectric barrier discharge (SDBD) system contained approximately 1000 ppm nitrate (NO₃⁻) and was designated as PAW1000. Diluted PAW solutions were applied to sprout crops—wheat (Triticum aestivum), barley (Hordeum vulgare), radish (Raphanus sativus), and broccoli (Brassica oleracea var. italica)—grown under hydroponic and soil-based conditions. PAW100 and PAW200 treatments enhanced growth, increasing fresh biomass by up to 26%, shoot length by 22%, and root length by 18%, depending on the species. In silico analysis identified nitrogen-responsive transcripts among several autophagy-related genes. Consistent with this, fluorescence microscopy of Arabidopsis thaliana GFP-StATG8 lines revealed increased autophagosome formation following PAW treatment. The growth-promoting effect of PAW was diminished in atg4 mutants, indicating that autophagy contributes to plant responses to PAW-derived ROS and RNS. Together, these findings demonstrate that diluted PAW generated by SDBD enhances biomass accumulation in sprout crops, and that autophagy plays a regulatory role in mediating PAW-induced physiological responses. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

Improving the thermal utilisation of organic production waste to generate energy is integral to solving one of the most pressing issues of our time: transitioning away from fossil fuels. In this context, the thermal utilisation of organic waste, particularly sewage sludge (SS) and lignin-containing by-products from the biochemical industry, is of considerable scientific and practical interest. This study provides a thorough analysis of the co-combustion processes involving SS, lignin-based pellets and briquettes, and their mixtures with various component ratios. The aim of the work is to evaluate the fuel properties, thermokinetic characteristics, and potential for synergistic interactions during joint fuel combustion, considering the mechanical impact on lignin during granulation. The aim is to optimise conditions for the thermal utilisation of industrial waste. The study employed standard analytical methods: the thermophysical properties of the fuels were determined; morphological analysis of the particle surface was conducted using scanning electron microscopy; and X-ray fluorescence analysis was performed to identify the inorganic oxide phase. It has been established that lignin briquettes have the highest lower heating value, exceeding that of lignin pellets and sewage sludge by 7% and 27%, respectively. Thermogravimetric analysis (TGA) in an oxidising atmosphere (air, heating rate of 10 °C/min) made it possible to determine the following key combustion parameters: the ignition temperature of the coke residue (T_i); the temperature at which oxidation is complete (T_b); the maximum combustion rate (R_max); and the combustion efficiency index (Q). The ignition temperature of the coke residue was 262.1 °C for SS, 291.8 °C for lignin pellets, and 290.0 °C for lignin briquettes. Analysis of co-combustion revealed non-linear behaviour in the thermograms, indicating synergistic effects, which are manifested by a decrease in the maximum combustion rate compared to the additive prediction, particularly in mixtures with a moderate lignin content (25–50%). It was established that the main synergistic interactions between the mixture components occurred during moisture evaporation and the combustion of coke residue. These results are valuable for designing and operating power plants that focus on co-combusting industrial organic waste, and they contribute to the development of thermal utilisation technologies within closed production cycles. Full article

To promote the transition to a cleaner energy structure and support the achievement of the “carbon peak and carbon neutrality” goals, concentrated solar power (CSP) technology has attracted increasing attention. The molten salt globe valve, as a key control component in CSP systems, faces significant challenges related to low-temperature salt crystallization and thermal stress control. This study proposes an active electromagnetic induction heating method based on a triangular double-helix cross-section coil to address issues such as molten salt blockage in the seal bellows and excessive thermal stress during heating. First, electromagnetic simulation comparisons show that the ohmic loss of the proposed coil is approximately 3.5 times and 1.8 times higher than that of conventional circular and rectangular coils, respectively, demonstrating superior heating uniformity and energy efficiency. Second, transient electromagnetic-thermal-fluid-structure multiphysics coupling analysis reveals that during heating, the temperature in the bellows seal region stabilizes above 543.15 K, exceeding the solidification point of the molten salt, while the whole valve reaches thermal stability within about 1000 s, effectively preventing local solidification. Finally, thermal stress analysis indicates that under a preheating condition of 473.15 K, the transient thermal shock stress on the valve body and bellows is reduced by 266.84% and 253.91%, respectively, compared with the non-preheating case, with peak stresses remaining below the allowable stress limit of the material, thereby significantly extending the service life of the valve. This research provides an effective solution for ensuring reliable operation of molten salt valves and improving the overall performance of CSP systems. Full article

Berries are a valuable source of health-promoting substances, including vitamins, microelements, and polyphenols. Optimising the extraction efficiency of these compounds during processing is crucial to minimise their loss into the waste stream. Ultrasound technology is recognised as a sustainable and promising tool for improving extraction; however, previous literature has not sufficiently addressed the optimal point of its application in fruit puree processing, and its impact on the sensory properties of the final product has only occasionally been explored. As one of the first reports, this study aimed to determine the optimal moment for ultrasound application within a puree production scheme. In the second stage of the experiment, four recipes based on strawberry and haskap berry were tested. The results demonstrated the potential for enhancing sensory quality of puree by using an ultrasound treatment. It was found that the ultrasound-treated purees showed significantly higher pectin levels and improved rheological properties, while the content of anthocyanins and L-ascorbic acid remained mainly unchanged. This indicates that the non-thermal nature of ultrasound treatment can induce positive changes from a sensory and rheological point of view without causing the degradation of health-promoting compounds, offering a viable strategy for improving berry puree quality. Full article

The use of large-aperture scanning mirrors for high-resolution and wide-swath imaging represents a major trend in Earth observation technology. However, to improve dynamic response performance, scanning mirror assemblies are highly lightweighted, resulting in reduced overall stiffness. This makes the mirror surface susceptible to thermal and inertial loads during operation, leading to degraded surface accuracy and poor imaging quality. Moreover, dynamic scanning mirror has the multi-disciplinary coupling effects and non-circular structural characteristics. It poses significant challenges for surface figure analysis. To address these issues, this paper proposes a surface analysis method for high-dynamic scanning mirrors under multi-physics coupling in non-circular domains. First, a finite element model of the mirror assembly is established based on the minimum aperture and angular velocity parameters. Through finite element analysis, the surface response of the scanning mirror assembly under thermal loads, dynamic inertial loads, and their coupled effects is quantitatively investigated. Subsequently, an analytical approach, which combines rigid-body displacement separation and Gram–Schmidt orthogonalization, is developed to construct non-circular Zernike polynomials, enabling high-precision fitting and reconstruction of the mirror’s dynamic surface distortions. Numerical experiments validate the accuracy of the model. Results show that for a scanning mirror with an aperture of 466 mm × 250 mm under the coupled condition of a 5 °C temperature rise and 50 N·mm torque, the surface figure achieves RMS < 2 nm and PV < 22 nm, with a fitting accuracy achieves 10⁻⁶. These results verify the accuracy and reliability of the proposed method. The surface analysis approach presented in this study provides theoretical guidance and a design framework for subsequent image quality evaluation and assurance. Full article