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Construction equipment operators (CEOs), who are required to work in seated positions for prolonged periods, often develop excessive mental fatigue, causing human error-related accidents, lower productivity, and psychological illnesses. However, the current practice for assessing fatigue is limited on construction sites. Previous studies utilizing smartwatches, electroencephalography, or eye-tracking technologies are intrusive and not convenient since they require operators to wear special devices, while vision-based solutions are sensitive to lighting conditions and have serious privacy concerns. There is a demand for continuously and accurately monitoring CEOs' mental fatigue levels without causing discomfort and aversion. This study introduces a noninvasive and noncontact smart cushion method to bridge the knowledge gap. We first developed a smart cushion system incorporating optical fiber sensors to collect human heartbeat and respiration data. Then, we adopted the Bidirectional Long-Short-Term Memory (BiLSTM) model to recognize fatigue states. An experiment was conducted in which data was collected from 16 subjects engaged in simulated excavation tasks. Experimental results demonstrate the feasibility of the proposed method, and the BiLSTM model obtained an accuracy of 94.0%. The proposed smart cushion method could also be convenient for understanding ergonomic risks resulting from prolonged sitting, a grave occupational health and safety problem that plagues various industries. Practical Applications: This study presents a smart cushion–based framework to continuously monitor the mental fatigue states of construction equipment operators (CEOs) during daily work. The proposed solution has clear advantages: it (1) is nonintrusive since it no longer requires sensors attached to the skin of operators, (2) is not sensitive to dynamic lighting conditions and does not generate privacy concerns, (3) is easy-to-use since it can be placed on the operator's seat or seatback and does not need additional power. Experimental results also demonstrated that the proposed Gaussian mixture model and Bidirectional Long-Short-Term Memory model can achieve effective data processing and accurate fatigue recognition. The proposed system can provide construction managers with a quantitative and reliable assessment tool to measure CEOs' mental workloads and support the intervention (e.g., worker shifts and breaks). If wirelessly connected to a smartphone, the smart cushion can conveniently provide early warnings to operators and their managers to alert the operators at stake to take breaks/rests to avoid mental fatigue-related ill consequences. Moreover, the proposed solution could be used to study and prevent the risks resulting from prolonged sitting, which is a grave occupational health and safety problem plaguing many industries. [ABSTRACT FROM AUTHOR]

Whether the digital infrastructure can resolve the conflict between environmental concerns and economic development is an urgent issue to be addressed in China. By taking the implementation of 'Broadband China' policy as a quasi-natural experiment and using 15 million policy statements to measure the environmental protection institution (EPI), this study investigates the environmental externality of urban digital infrastructure and the nonlinear supporting role of EPI. It is found that urban digital infrastructure is conducive to reducing regional energy intensity and carbon emission intensity. After a series of robustness tests, the conclusions are still credible. Mechanism analysis shows that promoting the structural upgrading of the service industry, accelerating the intensive development of the manufacturing industry and improving the quality of regional innovation are three impact channels. Furthermore, the EPI plays a nonlinear supporting role in the process of digital infrastructure's promoting regional energy saving and emission reduction. This study incorporates the supply of environmental protection institution into the analytical framework, thus providing a new perspective for comprehending the externality of digital infrastructure and environmental protection institution. [ABSTRACT FROM AUTHOR]

This paper establishes a high-fidelity and efficient Computational Fluid Dynamics (CFD) numerical method (AL-LDS-nnew) for wind turbine wake by combining the actuator line (AL), the localized dynamic Smagorinsky (LDS) sub-grid scale (SGS), and the new generation nnew vortex identification method under the framework of large eddy simulation. The model advantages are encouraging: 1) In terms of turbine modeling, the AL model is adopted to replace the traditional threedimensional solid model, which avoids solving the boundary layer on the blade surface and improves computational efficiency; 2) In terms of wake simulation, the LDS SGS model is used to model turbulence, reducing vortex dissipation and further improving the refinement of turbine wake; 3) In terms of vortex identification, the new generation nnew vortex identification method avoids the difficult threshold selection in previous vortex identification and captures more refined vortex structures. The accuracy of the model is validated against published data of a NREL 5 MW wind turbine, and then extended to simulate the wake interference of tandem twin-rotor turbines by changing the pitch angle of the upstream wind turbine (WT1). The influence mechanisms between array wake interference and energy conversion efficiency under the pitch strategy are explored, demonstrating the AL-LDS-finew coupling method is computationally accurate and efficient for simulating the complex wake interference. From analyses, the pitch strategy can effectively suppress the wake effect of the upstream turbine (WT1) and increase the power output of the downstream turbine (WT2), thus improving the overall output power of the array farm. Compared with the non-pitch condition (0 pitch angle), a pitch angle of (2°) maximizes the global energy conversion efficiency of the twin-rotor array: power augmentation by 0.29%, and thrust reduction by 5%. This optimal state reduces the fatigue load of the turbine and is more conducive to long-term operation. The findings, whilst preliminary, encourage the use of turbine pitch strategies in the wind farm planning and operation. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic behaviour of a tension leg platform (TLP) under different damaged mooring conditions is studied. ANSYS/AQWA is used to develop a nonlinear hull-tendon model, which can consider the coupling effects between the platform motion and mooring line dynamics at each time step. Noteworthily, an effective method for simulating tendon breakage is proposed. The transient effects of tendon breakage, the transient responses of the platform for simultaneous and progressive tendon breakage, and the performance changes of the platform after tendon breakage are investigated. The results show that the tendon breakage has a significant influence on the dynamic responses and performance of the platform. Therefore, the effects of tendon breakage on the platform should be examined in advance to prevent the safety of the platform from being threatened. [ABSTRACT FROM AUTHOR]

GNSS spoofing has become a significant security vulnerability threatening remote sensing systems. Hardware fingerprint-based GNSS receiver identification is one of the solutions to address this security issue. However, existing research has not provided a solution for distinguishing GNSS receivers of the same specification. This paper first theoretically proves that the CSACs (Chip-Scale Atomic Clocks) used in GNSS receivers have unique hardware noise and then proposes a fingerprinting scheme based on this hardware noise. Experiments based on the neural network method demonstrate that this fingerprint achieved an identification accuracy of 94.60% for commercial GNSS receivers of the same specification and performed excellently in anomaly detection, confirming the robustness of the fingerprinting method. This method shows a new real-time GNSS security monitoring method based on CSACs and can be easily used with any commercial GNSS receivers. [ABSTRACT FROM AUTHOR]

Numerous factors influence mountain biodiversity variation across elevational gradients and recognizing the relative importance is vital for understanding species distribution mechanisms. We examined oribatid mites at nine elevations (from 600 to 2200 m a.s.l) and four vegetation types from mixed coniferous and broad‐leaved forests to alpine tundra on Changbai Mountain. We assessed the contribution of environmental factors (climatic and local factors) and spatial processes (geographic or elevation distances) to oribatid mite community assembly and identified 59 oribatid mite species from 38 families and 51 genera. With increasing elevation, species richness and the Shannon index declined significantly, whereas abundance followed a hump‐shaped trend. Soil TP, NH4+‐N, MAT, MAP, and elevation were the critical variables shaping oribatid mite communities based on random forest analysis. Moreover, environmental and spatial factors, and oribatid mite communities were significantly correlated based on Mantel and partial Mantel tests. Local characteristics (3.9%), climatic factors (1.9%), and spatial filtering (8.8%) played crucial roles in determining oribatid mite communities across nine elevational bands (based on variation partitioning analyses of abundance data). Within the same vegetation types, spatial processes had relatively little effects, with local characteristics the dominant drivers of oribatid mite community variation. Environmental and spatial filters together shape oribatid mite community assembly and their relative roles varied with elevation and vegetation type. These findings are crucial for the conservation, restoration, and management of Changbai mountain ecosystems in the context of climate change, along with the prediction of future vertical biotic gradient pattern evolution. [ABSTRACT FROM AUTHOR]

Using panel data from 30 provinces in China from 2005 to 2020, this paper uses a spatial double difference model to evaluate the policy impact of the "grain-to-feed" policy on feed grain production in pilot areas and adjacent spatial areas. Research has found that the "grain-to-feed" policy has a significant impact on the feed grain production in pilot areas and can significantly increase the feed grain production in pilot areas by about 2.71 million tons. The "grain-to-feed" policy has strengthened the positive connection between pilot areas and adjacent pilot areas, increased feed grain production, and has a significant spatial spillover effect. Robustness analysis shows that whether using different methods to measure spatial adjacency or using different standards to distribute subsidies, the "grain-to-feed" policy can significantly increase feed grain production, narrow the supply and demand gap of feed grain, and ensure feed grain security. Further analysis shows that the "grain-to-feed" policy can not only ensure the security of feed grain for the current and next periods but also promote the increase in farmers' income, which is long-term and sustainable. Compared with non-pilot areas, the "grain-to-feed" policy can mitigate the negative impact of wage–price signals on feed grain production in pilot areas. It is recommended that government departments accelerate the transformation of food security concepts, establish a "Big Food Perspective", gradually promote the pilot of the "grain-to-feed" policy nationwide, increase the subsidy amount of the "grain-to-feed" policy, increase financial support for scientific and technological research and achievement transformation in the field of feed grain, prevent the impact of economic price signal fluctuations on feed grain production, and effectively ensure the security of feed grain in China. [ABSTRACT FROM AUTHOR]

Since the energy sector is the primary source of carbon emissions, achieving low carbon emission reduction and meeting the carbon peak and carbon neutrality targets require a full grasp of the symbiotic relationship between the traditional and new energy industries. This study analyzes data from China's conventional energy industries and the new energy industry from 2005 to 2021 to examine the symbiosis between these two sectors of the economy and its implications. The Lotka–Volterra model is used to determine the symbiosis between industries and further select provinces with industrial symbiosis to analyze the relationship between industrial symbiosis and consumer demand. The results show that, first, 28 provinces in China have industrial symbiosis, of which only four provinces are commensal symbiosis and the remaining 24 provinces are parasitic symbiosis. Second, whereas the parasitic symbiosis will impede the growth of consumer demand, the commensal symbiosis between the new energy industry and the traditional energy industry can greatly encourage the growth of consumer demand. In addition, the promotion effect of commensal symbiosis on consumer demand is mainly manifested in government consumption, while parasitic symbiosis has a dampening effect on residential consumption, government consumption, and investment. Finally, under the regulation of ecological carbon sequestration, parasitic symbiosis can promote the growth of consumer demand. Therefore, the government should continue to promote industrial symbiosis and ecological carbon sequestration and, in this way, promote the growth of consumer demand. [ABSTRACT FROM AUTHOR]