Search Results (696)

Field surface fluctuations and crop residues can induce significant random vibrations of no-tillage planters, which may negatively affect seed implantation stability and crop yield. At present, it is difficult to understand the extent to which the working components of a no-tillage planter affect its vibration, and how to reduce the influence of vibration on the quality of the no-tillage seeding is a critical problem. The main factors affecting the vibration of no-tillage planters were studied by tractor engine vibration source impact analysis experiments, no-tillage planter structural vibration source experiments, and light and heavy no-tillage configuration vibration source analysis experiments. The results show that the effects of the ground wheels, the fertilizing and stubble breaking and cleaning devices, the packer wheels, and the power output shaft gradually diminish. The resonant frequencies of the tractor–no-tillage planter system were 68.36 Hz and 67.38 Hz. Furthermore, this study provided a relative assessment of the correlation between planter downforce and its vibration intensity. To sum up, the multi-source vibration impact analysis method proposed an effective method for studying the contribution of individual components to the overall vibration behavior of no-tillage planters. It provides a theoretical basis for the optimization design of the vibration damping system. Full article

In 2024, Brazil experienced record-breaking wildfire activity, underscoring the escalating influence of climate change. This study examines the long-range transport of wildfire-generated aerosol plumes to São Paulo, combining multi-platform observations to trace their origin and properties. During August and September—a period marked by intense fire outbreaks in Pará and Mato Grosso do Sul—lidar measurements performed at São Paulo detected pronounced aerosol plumes. To investigate their source and characteristics, we integrated data from the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, HYSPLIT back-trajectory modeling, and ground-based AERONET and Raman lidar measurements. Aerosol properties were derived from aerosol optical depth (AOD), Ångström exponent, and lidar ratio (LR) retrievals. Back-trajectory analysis identified three transport pathways originating from active fire zones, with coinciding AOD values (0.7–1.1) and elevated LR (60–100 sr), indicative of dense smoke plumes. Compositional analysis revealed a significant black carbon component, implicating wildfires near Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as probable emission sources. These findings highlight the efficacy of satellite-based lidar systems, such as Atmospheric Lidar (ATLID) onboard EarthCARE, in atmospheric monitoring, particularly in data-sparse regions where ground instrumentation is limited. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article

Seed rope direct seeding technology is a precision seeding method that can accurately mix and arrange multiple varieties based on specific grain spacing and quantity, making it suitable for precision breeding and variety comparison studies. As seed ropes serve as the crucial seed encapsulation material in seed rope direct seeding, this study employed a multi-faceted approach to investigate the dynamic degradation of nonwoven fabric and paper material seed ropes under diverse environmental conditions as well as their adhesion properties with Ultisols, Oxisols, and the Substrate in this seeding technique. Firstly, the degradation dynamics were systematically analyzed using image-based surface area detection, breaking force measurement, and organic carbon content analysis. Secondly, the process of seed rope laying was simulated by modeling the sliding friction and adhesion forces during the detachment of soil slurry. The laying motion was simulated considering both sliding friction (during the uniform-speed interaction between the seed rope and soil slurry) and adhesion (during upward detachment), providing crucial reference values for optimizing the rope-breaking mechanism in field applications. The study yielded several significant findings: In natural environments, Wood pulp paper seed rope degrades significantly faster than nonwoven fabric, with a degradation cycle of only 5.68 days in winter (approximately 34% of the degradation cycle of nonwoven fabric) and 4.70 days in summer (approximately 78% of the degradation cycle of nonwoven fabric). The main effect of seed viability on the degradation rate of seed tapes was not statistically significant. The degradation of Wood pulp paper seed rope was relatively predictable in indoor settings but exhibited notable fluctuations outdoors. The peak friction occurred at 35% soil moisture content, with values of 1.22 N for Wood pulp paper seed rope and 2.08 N for nonwoven fabric when interacting with Oxisols; nonwoven ropes demonstrated stronger adhesion than Wood pulp paper seed rope in the Substrate (at moisture contents of 25–30% and 40–45%) and Oxisols (at 35–45% moisture). In Ultisols, nonwoven fabric only showed superior adhesion compared to Wood pulp paper seed rope at 35–45% moisture, while Wood pulp paper seed rope exhibited better adhesion in other moisture ranges. Full article

China’s major function-oriented zoning (MFOZ) serves as a crucial policy instrument for functional regulation of land use, playing a significant role in the latest territorial spatial planning. Studies on the implementation of MFOZ have been conducted since its release in 2012, but there is a lack of comprehensive methods to assess the effectiveness of its implementation. In China, the newly initiated City Examination provides novel technical support for verifying MFOZ planning, addressing the gap in comprehensive evaluation methodologies and channels. This study proposes a comprehensive urban assessment framework and a major function classification approach based on City Examination data, enabling the identification of implementation deviations in MFOZ planning based on the current urban conditions reflected by City Examination. The methodology incorporates dimensionality reduction, multi-indicator clustering, entropy-weighted overlays, and natural break classification techniques and examines the degree of strategic deviation in China’s MFOZ through a comprehensive and systematic assessment. Due to the timeliness and long-term nature City Examination data, the method allows for the long-time dynamic tracking and evaluation of the real-time progress in MFOZ. Empirical analysis of Hubei Province revealed that 77.9% of its urban development aligns with the 2011 MFOZ scheme while demonstrating discernible deviation types and hierarchical discrepancies, with geographically clustered patterns observed among cities exhibiting such deviations. Full article

This study developed a deep learning–based water level recognition model using Closed-Circuit Television (CCTV) footage. The model focuses on real-time water level recognition in agricultural reservoirs that lack automated water level gauges, with the potential for future extension to flood forecasting applications. Video data collected over approximately two years at the Myeonggyeong Reservoir in Chungcheongbuk-do, South Korea, were utilized. A semantic segmentation approach using the U-Net model was employed to extract water surface areas, followed by the classification of water levels using Convolutional Neural Network (CNN), ResNet, and EfficientNet models. To improve learning efficiency, water level intervals were defined using both equal spacing and the Jenks natural breaks classification method. Among the models, EfficientNet achieved the highest performance with an accuracy of approximately 99%, while ResNet also demonstrated stable learning outcomes. In contrast, CNN showed faster initial convergence but lower accuracy in classifying complex intervals. This study confirms the feasibility of applying vision-based water level prediction technology to flood-prone agricultural reservoirs. Future work will focus on enhancing system performance through low-light video correction, multi-sensor integration, and model optimization using AutoML, thereby contributing to the development of an intelligent, flood-resilient water resource management system. Full article

Wastewater containing heavy metals can come from a variety of sources and is extremely toxic and hard to break down. Conventional treatment methods can easily result in secondary pollution and are expensive. The research on magnetic chitosan composites, a new adsorbent in the treatment of heavy metal wastewater, is methodically reviewed in this paper. It offers a theoretical foundation for the creation of more environmentally friendly and effective wastewater treatment technology by examining its preparation and modification technology, adsorption mechanism, and application performance. This paper provides a summary of the technology used to prepare and modify magnetic chitosan composites. Both the cross-linking and co-precipitation methods are thoroughly examined. A summary of the fundamental process of heavy metal ion adsorption is provided, along with information on the chemical and physical impacts. Of these, chemical adsorption has been shown to work well with the majority of heavy metal adsorption systems. According to application research, magnetic chitosan exhibits good adaptability in real-world industrial wastewater treatment and has outstanding adsorption performance for various heavy metal ion types and multi-metal coexistence systems (including synergistic/competitive effects). Lastly, the optimization of the material preparation and modification process, the mechanism influencing the various coexisting ion types, and the improvement of regeneration ability should be the main areas of future development. Full article

Trustworthy product quality data forms the foundation of digital and distributed manufacturing, yet current centralized product quality management (PQM) systems remain vulnerable to data manipulation, traceability breaks, single points of failure, and related adverse effects. To clarify how blockchain can address these weaknesses, this paper presents a systematic review of blockchained product quality management (BPQM). Firstly, the paper groups the architectures and models related to BPQM and proposes an ISA 95-aligned reference framework that secures a real-time quality data exchange. Secondly, seven key BPQM enablers are analyzed, including (1) visual intelligence-based quality inspection, (2) cyber–physical twinning and parallel control of manufacturing systems, (3) blockchained agent modeling and secure data sharing, (4) multi-level blockchain mapping, (5) smart contract-based decentralized system configuration and operation, (6) artificial intelligence-based decentralized BPQM applications, and (7) traceability of process coordination and control. Thirdly, through analysis of social barriers and technological challenges, four research directions are identified, namely, (1) optimal granularity of data in system configuration; (2) smart contracts for self-organizing intelligence; (3) balancing system security, cost, and performance; and (4) interoperability and integration with legacy systems. It is expected that this paper lays a solid foundation for the practical use of blockchain in PQM engineering. Full article

With rapid urbanization, the development of Urban Underground Space (UUS) has become essential to addressing various urban challenges. However, the accelerated expansion of UUS has also introduced problems such as duplicated infrastructure, functional deficiencies, and underutilized spaces. Fundamentally, these issues result from imbalances between the supply and demand for UUS, a phenomenon particularly pronounced in the central areas of major cities. Therefore, employing scientific methods to accurately identify and quantify these gaps is crucial. Leveraging recent advances in remote sensing and point-of-interest (POI) data, this study constructs a multi-source data-driven framework for assessing UUS supply–demand relationships, applied using a grid-based analysis to the central urban area of Nanjing. The results indicate that both the highest supply capacity and demand intensity occur in Xinjiekou Street in Nanjing’s Old City. Most high and medium–high supply and demand zones are concentrated in the Old City. Areas with prominent supply–demand conflicts are identified and classified into five types using the Jenks natural breaks method, further categorized into three groups based on their spatial characteristics, with tailored development strategies proposed accordingly. The proposed evaluation framework provides a robust scientific approach for analyzing UUS supply–demand relationships, offering significant theoretical and practical value for refined urban governance in large cities with extensive data availability. Full article

Facing challenges of low efficiency and severe wear in deep hard formations with conventional drilling bits, this study investigates the synergistic rock-breaking technology combining a pulsed CO₂ laser with mechanical bits. The background highlights the need for novel methods to enhance drilling speed in high-strength, abrasive strata where traditional bits struggle. The theoretical analysis explores the thermo-mechanical coupling mechanism, where pulsed laser irradiation rapidly heats the rock surface, inducing thermal stress cracks, micro-spallation, and strength reduction through mechanisms like mineral thermal expansion mismatch and pore fluid vaporization. This pre-damage layer facilitates subsequent mechanical fragmentation. The research employs finite element numerical simulations (using COMSOL Multiphysics with an HJC constitutive model and damage evolution criteria) to model the coupled laser–mechanical–rock interaction, capturing temperature fields, stress distribution, crack propagation, and assessing efficiency. The results demonstrate that laser pre-conditioning significantly achieves 90–120% higher penetration rates compared to mechanical-only drilling. The dominant spallation mechanism proves energy-efficient. Conclusions affirm the feasibility and significant potential of CO₂ laser-assisted drilling for deep formations, contingent on optimized laser parameters, composite bit design (incorporating laser transmission, multi-head layout, and environmental protection), and addressing challenges, like high in-situ stress and drilling fluid interference through techniques like gas drilling. Future work should focus on high-power laser downhole transmission, adaptive control, and rigorous field validation. Full article

Packing food into lunch boxes requires the correct portion to be selected. Food items such as fried chicken, eggs, and sausages are straightforward to manipulate when packing. In contrast, deformable objects like spaghetti can give challenges to lunch box packing due to their fragility and tendency to break apart, and the fluctuating weight of noodles. Furthermore, achieving the correct amount is crucial for lunch box packing. This research focuses on self-learned grasping by a robotic arm to enable the ability to autonomously predict and grasp deformable objects, specifically spaghetti, to achieve the correct amount within specified ranges. We utilize deep reinforcement learning as the core learning. We developed a custom environment and policy network along a real-world scenario that was simplified as in a food factory, incorporating multi-sensors to observe the environment and pipeline to work with a real robotic arm. Through the study and experiments, our results show that the robot can grasp the spaghetti within the desired ranges, although occasional failures were caused by the nature of the deformable object. Addressing the problem under varying environmental conditions such as data augmentation can partially help model prediction. The study highlights the potential of combining deep learning with robotic manipulation for complex deformable object tasks, offering insight for applications in automated food handling and other industries. Full article

Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a dual innovation: a real-time break detection method and a low-cost multi-core parallel sensing system design based on ACS712 Hall sensors, achieving high detection accuracy (100% with zero false positives in tests). Unlike conventional techniques, the approach leverages current differential (ΔI) monitoring across parallel cores, triggering alarms when ΔI exceeds I_rate/2 (e.g., 0.3 A for 0.6 A rated current), corresponding to a voltage deviation ≥ 110 mV (normal baseline ≤ 3 mV). The core innovation lies in the integrated sensing system design: by optimizing the parallel deployment of ACS712 sensors and LMV324-based differential circuits, the solution reduces hardware cost to USD 3 (99.99% lower than fiber optic systems), payload by 18%, and power consumption by 23% compared to traditional methods. Post-fault cable temperatures remain ≤56 °C, ensuring safety margins. The 4-core architecture enhances mean time between failures (MTBF) by 83% over traditional systems, establishing a new paradigm for low-cost, high-reliability sensing systems in terrestrial tethered UAV cable health monitoring. Preliminary theoretical analysis suggests potential extensibility to underwater scenarios with further environmental hardening. Full article

The aircraft power supply system plays a crucial role in maintaining the stability and safety of airborne avionics. With the evolution toward more electric and all-electric aircraft, its architecture increasingly adopts symmetrical configurations, such as dual-redundant paths and three-phase balanced outputs. However, these symmetry-based designs are often disrupted by diverse fault mechanisms encountered in complex operational environments. This review contributes a comprehensive and structured analysis of how such fault events lead to symmetry-breaking phenomena across different subsystems, including generators, converters, controllers, and distribution networks. Unlike previous reviews that treat faults in isolation, this study emphasizes the underlying physical mechanisms and hierarchical fault propagation characteristics, revealing how structural coupling and multi-physics interactions give rise to failure modes. The paper concludes by outlining future research directions in symmetry-aware fault modeling and intelligent maintenance strategies, aiming to address the growing complexity and reliability demands of next-generation aircraft. Full article

This study examines how Multi-Scalar Nature-Based Regenerative Solutions (M-NbRS) can realign urban–industrial systems with planetary boundaries to mitigate Earth system destabilization. Using integrated systems analysis, we document three key findings: (1) global material flows show only 9% circularity amid annual extraction of 100 billion tons of resources; (2) Earth system diagnostics reveal 28 trillion tons of cryosphere loss since 1994 and 372 Zettajoules of oceanic heat accumulation; and (3) meta-analysis identifies accelerating biosphere integrity loss (61.56 million hectares deforested since 2001) and atmospheric CO₂ concentrations reaching 424.61 ppm (2024). Our Vicious Cycle Atlas of Fragility framework maps three synergistic disintegration pathways: metabolic overload from linear resource flows exceeding sink capacity, entropic degradation through high-entropy waste driving cryospheric collapse, and planetary boundary transgression. The M-NbRS framework counters these through spatially nested interventions: hyper-local urban tree canopy expansion (demonstrating 0.4–12 °C cooling), regional initiatives like the Heart of Borneo’s 24 million-hectare conservation, and global industrial controls maintaining aragonite saturation (Ωarag > 2.75) for marine resilience. Implementation requires policy innovations including deforestation-free supply chains, sustainability-linked financing, and ecological reciprocity legislation. These findings provide an evidence base for transitioning industrial–urban systems from drivers of Earth system fragility to architects of regeneration within safe operating spaces. Collectively, these findings demonstrate that M-NbRS offer a scientifically grounded, policy-actionable framework for breaking the vicious cycles of Earth system destabilization. By operationalizing nature-based regeneration across spatial scales—from street trees to transboundary conservation—this approach provides measurable pathways to realign human systems with planetary boundaries, offering a timely blueprint for industrial–urban transformation within ecological limits. Full article