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The online measurement of moisture content for grains is an essential technology to realize real-time tracking and control, improve drying quality and reduce energy consumption of the drying process. To improve the measurement accuracy and reliability of the dynamic measurement process as well as expand the application scope of the device, the present work constructed an experimental equipment for determining dynamic resistance characteristics of a single grain. The relations between moisture content and real-time resistance waveform were revealed, and an analytical calculation method of peak value and peak area of waveform was proposed, which correctly revealed the electrical measurement properties of grain. The results demonstrated that the gap width between the electrodes had large influence on the sensor’s performance. Moreover, an online measuring device was developed based on the experimental analysis and calculation method, and the test results in both lab and field for different grains showed that online real-time absolute measurement error are within ±0.5% in the varied moisture content (10–35%w.b.) and the temperature (−20–50 °C). The main results and the developed device might provide technical support for developing intelligent grain drying equipment.

Flat bands have empowered novel phenomena such as robust canalization with strong localization, high-collimation and low-loss propagation. However, the spatial symmetry protection in photonic or acoustic lattices naturally forces flat bands to manifest in pairs aligned at an inherently specific angle, resulting in a fixed bidirectional canalization. Here, we report an acoustic flat-band metasurface, allowing not only unidirectional canalization at all in-plane angles but also robust tunability in band alignment. The twist, tilt, and skew angles of the bilayer metasurface can be flexibly controlled to break both in-plane and out-of-plane spatial symmetries. These features can thereby turn arbitrary twist angles between bilayers into ‘magic angles’, while maintaining all unidirectional canalization and band alignment tunability. This work may significantly contribute to pushing twisted moiré physics into higher dimensions and facilitate the application of advanced acoustic or optical devices. Flat bands have empowered novel phenomena such as canalization, high-collimation and low-loss propagation. Here, the authors propose the concept of symmetry broken moiré systems, which overcomes the limitations of fixed flat bands, enabling multiple and unidirectional canalizations.

This study evaluated the knowledge, attitudes, and practices (KAP) of resident and intern physicians regarding narrative medicine in Yunnan Province, China, in August 2024. A cross-sectional design was employed, utilizing a self-designed questionnaire to gather demographic information and assess knowledge and attitude. The practice was evaluated by Narrative Competence Scale. Out of 482 valid responses, 62.03% of participants were female, with 43.58% in their first year of residency and 46.06% having received narrative medicine training. The mean scores for knowledge, attitude, and practice were 49.32 ± 11.72 (possible range: 14–70), 21.99 ± 2.66 (possible range: 6–30), and 143.87 ± 23.52 (possible range: 27–189), respectively. Structural equation modeling revealed significant relationships: knowledge positively and directly impacted both attitudes (β = 0.550, P  < 0.001) and practices (β = 0.341, P  < 0.001), while attitudes directly influenced practices (β = 0.190, P  < 0.001). Furthermore, knowledge affected practice indirectly through attitudes (β = 0.104, P  < 0.001). The study concluded that resident and intern physicians exhibited moderate knowledge, moderate attitudes, and low engagement with narrative medicine. It suggested that incorporating narrative medicine into medical training could enhance KAP, thereby improving patient-centered care and communication in clinical settings.

Moiré quasicrystals, formed by stacking periodic structures with a relative twist between them, exhibit many exotic phenomena. Their quasiperiodicity leads to effects such as light localization-delocalization transitions, superconductivity, topological states, and quasiband dispersion. However, weak interlayer interactions, the scalar nature of acoustic fields, and longer wavelengths severely limit the demonstration of these phenomena in acoustics. Here, we report an acoustic moiré quasicrystal that not only achieves a localization-delocalization transition, but also enables wave propagation shifting from diffusion to canalization or localization as a function of the quasicrystal geometry. Unlike conventional two-dimensional materials, the designed sublattice provides tailorable anisotropy and spatial broken symmetry, allowing quasicrystal structures to exhibit reconfigurable nontrivial dispersion. Furthermore, by introducing a uniform tilt angle in the unit cells breaks the spatial symmetry of the moiré quasicrystal, resulting in partial attenuation and disappearance of the wave within the localization pattern. Our findings pave a new avenue for controlling the properties of acoustic wave patterns, and benefit potential applications in energy transfer, subwavelength wave propagation, and highly sensitive sensors. Moiré quasicrystals exhibit many exotic phenomena. Here, the authors report an acoustic moiré quasicrystal that not only achieves a localization-delocalization transition, but also enables wave propagation shifting from diffusion to canalization or localization.

In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control method for paddy drying, but its implementation in industrial-scale drying is hindered by its high computational cost. This study aims to address this challenge by proposing a deep-learning-based model predictive control (DL-MPC) strategy for paddy drying. By establishing a mapping relation between the inlet and outlet paddy moisture content and paddy flow velocity, a DL-MPC strategy suitable for multistage counter-flow paddy drying systems is proposed. DL-MPC systems are developed using long short-term memory (LSTM) neural networks and trained using datasets from single-drying-stage and multistage drying systems. Simulation and analysis are conducted, followed by verification experiments on a 5HNH-15 multistage counter-flow paddy dryer. The results show that the DL-MPC system significantly improves computational speed while achieving satisfactory control performance. The predicted paddy flow velocity exhibits a smooth variation and matches field data obtained from multiple transition points, confirming the effectiveness of the designed DL-MPC system. The mean absolute error between the predicted and actual paddy moisture content under the DL-MPC system is 0.190% d.b., further supporting the effectiveness of the control system.

In this study, a novel kinetic model is established to investigate the dynamic characteristic of the ball screw feed system by considering the thermal deformation of bearing joints, screw-nut joints and screw shaft. Based on the Hertz contact theory, the relationship between elastic restoring force and axial deformation of bearing joints and screw-nut joints is obtained, respectively. Then the dynamic characteristics of the kinetic equation are analyzed by Runge–Kutta method. The vibration characteristics of the feed system with and without thermal deformation are analyzed, and the results indicate that the amplitude becomes larger when thermal deformation is considered. The motion state of the feed system at different frequencies is analyzed, and the results show that with the change of frequency, the motion state of the system will appear period-doubling motion, quasi-periodic motion and chaotic motion. Finally, the influence of different parameters on the vibration characteristics of the system is discussed.

The improvement of the design and operation of energy conversion systems is a theme of global concern. As an energy intensive operation, industrial agricultural product drying has also attracted significant attention in recent years. Taking a novel industrial corn drying system with drying capacity of 5.5 t/h as a study case, based on existing exergoeconomic and exergetic analysis methodology, the present work investigated the exergetic and economic performance of the drying system and identified its energy use deficiencies. The results showed that the average drying rate for corn drying in the system is 1.98 gwater/gdry matter h. The average exergy rate for dehydrating the moisture from the corn kernel is 345.22 kW and the exergy efficiency of the drying chamber ranges from 14.81% to 40.10%. The average cost of producing 1 GJ exergy for removing water from wet corn kernels is USD 25.971, while the average cost of removing 1 kg water is USD 0.159. These results might help to further understand the drying process from the exergoeconomic perspective and aid formulation of a scientific index for agricultural product industrial drying. Additionally, the results also indicated that, from an energy perspective, the combustion chamber should be firstly optimized, while the drying chamber should be given priority from the exergoeconomics perspective. The main results would be helpful for further optimizing the drying process from both energetic and economic perspectives and provide new thinking about agricultural product industrial drying from the perspective of exergoeconomics.

Water scarcity poses a significant challenge for people living in arid areas. Despite the effectiveness of many bioinspired surfaces in promoting vapor condensation, their water-harvesting efficiency is insufficient. This is often exacerbated by overheating, which decreases the performance in terms of the micro-droplet concentration and movement on surfaces. In this study, we used a spotted amphiphilic surface to enhance the surfaces’ water-harvesting efficiency while maintaining their heat emissivity. Through hydrophilic particle screening and hydrophobic groove modifying, the coalescence and sliding characteristics of droplets on the amphiphilic surfaces were improved. The incorporation of boron nitride (BN) nanoparticles further enhanced the surfaces’ ability to harvest energy from condensation. To evaluate the water-harvesting performance of these amphiphilic surfaces, we utilized a real-time recording water-harvesting platform to identify microscopic weight changes on the surfaces. Our findings indicated that the inclusion of glass particles in hydrophobic grooves, combined with 1.0 wt.% BN nanoparticles, enhanced the water-harvesting efficiency of the amphiphilic surfaces by more than 20%.

How to expand an owner’s market demand for BIM and fundamentally mobilize an owner’s application enthusiasm is of great significance to the high-quality development and effective promotion of BIM. Taking the Theory of Planned Behavior (TPB) as the basic framework and integrating the Technology Acceptance Model (TAM), we build a theoretical model of an owner’s adoption behavior mechanism for BIM technology. The theoretical model is tested by the partial least squares structural equation model (PLS-SEM). The research results show that the perceived usefulness of BIM technology by an owner is the most significant factor that affects the owner’s behavioral intention to apply BIM technology. The influence of attitude on behavioral intention is very weak and not significant. Subjective norms can significantly and positively affect an owner’s adoption intention. The perceived ease of use has a positive impact on the adoption intention of BIM technology, but its role is very limited. The adoption intention of an owner can positively affect adoption behavior and has a direct driving effect. The research results can improve the relevant research on BIM, encourage an owner to actively participate in the development process of BIM, and further promote the comprehensive promotion and application of BIM.

Nutrients may be eliminated from ice when liquid water is freezing, resulting in enhanced concentrations in the unfrozen water. The nutrients diluted from the ice may contribute to accumulated concentrations in sediment during winter and an increased risk of algae blooms during the following spring and summer. The objective of this study was to evaluate the influence of ice cover on nitrogen (N) and phosphorus (P) concentrations in the water and sediment of a shallow lake, through an examination of Ulansuhai Lake, northern China, from the period of open water to ice season in 2011–2013. The N and P concentrations were between two and five times higher, and between two and eight times higher, than in unfrozen lakes, respectively. As the ice thickness grew, contents of total N and total P showed C-shaped profiles in the ice, and were lower in the middle layer and higher in the bottom and surface layers. Most of the nutrients were released from the ice to liquid water. The results confirm that ice can cause the nutrient concentrations in water and sediment during winter to increase dramatically, thereby significantly impacting on processes in the water environment of shallow lakes.